Product Description
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface ... Read more

Customer Reviews (36)

DM01
I found many new interesting subjects readingthis book. It helps me to look for other sources for information. It was really useful!

Dissapointed
I'm about 1/2 way through the book and do intend to finish it.However, I must say, this is very tough reading.I think the author tries not to use a lot of math and algorithmic speak but ends up using descriptions that aren't very clear or easy to follow.

Plus, this book really does require a background in this topic.Perhaps a few books as pre-requisites would make this material easier to understand.

The author separates the more math intensive stuff in paragraphs marked as optional but to be honest, these are the sections that I found easier to understand.

Anyway, I really wanted to like this book.But unfortunately, I did not.

Perfect Introduction to Data Mining
This book is a great introduction to the field of Data Mining.I read it cover to cover, and found it hard to put down.The Weka platform is easy to use and well explained in this book.I have since journeyed into other Data Mining and Machine Learning tools, but I fall back on this book for reference.Every data scientist should have this book on their shelf.

Excellent Beginning Text for Software Engineers
I chose this book after looking at a number of options. I was not disappointed. The text is clearly written for individuals with an bachelor-level education in computer science. The author prefers pseudocode and text explanations of algorithms to equations, and when he does use equations they use clear, commonly understandable notation rather than the terse greek alphabet soup preferred by many of the more mathematically oriented authors.

It should be pointed out that about 10% of the text of this book is devoted simply as a user manual for an open source MLA package called Weka. When I first realized this I almost flipped; I really didn't want a book that was devoted to gaining a surface understanding of a particular implementation of a set of algorithms. After reading through, I can tell you it is not. All the algorithms are explained well enough that you could implement them and work out simple examples on paper.

I should note also that Weka, as well as a lot of the algorithms in this book, don't parallelize well (or obviously). This is an excellent point to get your feet wet and do some exploratory analysis, but if you're past that point and want to learn about crunching big (TB+) data you should look elsewhere.

One area that the text does not cover (and, for many software engineers this is not a fault) is some of the mathematics behind some of the algorithms the author proposes. For instance, in the author's description of linear regression using SGD he glosses over the math of actually calculating the gradient by saying "there's a matrix inversion involved and its available in prepackaged software." I'm not saying this is bad, because if you're a good software engineer the first thing you'll do it look for an existing implementation that you can alter to fit your needs, so he's right. It just may not be what mathematicians or more theory-oriented computer scientists expect.

Practice and theory of data mining
I am engaged in my first major data mining project, and as I read Witten's & Frank's book, page after page of experience answer the questions that I had been asking myself, then giving more. The book has given me the confidence to grow my abilities. Both Ian Witten and Eibe Frank have been drivers in developing the interface and collection of data mining tools called Weka developed at the University of Waikato, New Zealand.Their depth of experience shine in the book.The book has two parts.Part 1, Machine learning tools and techniques, guides the reader through the SEMMA data mining methodology (not specifically stated).Part 2, the WEKA machine learning workbench, is a guide into Weka, with detailed commentary to the underlying data mining method and theory.The contents is comprehensive, giving a detailed outline of the story line, thus giving the reader the overview and context needed to find sections of intererst rapidly. I have used other data mining texts, however the solid practical advice offered in this book sets it apart.I wish I had picked this book up twelve months ago. ... Read more

Product Description

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

Customer Reviews (16)

Buen libro introductorio, buena guia
Como libro introductorio me ha servido mucho, es mi primera clase de Mineria de Datos y ha sido buena guia, incluye ejercicios que dan una idea clara del trabajo.

Good Book , not for beginners
To understand this book you need some amount of advanced Stats knowledge , i work in the field of Data warehousing and was expecting to relate it to my present field ,I did some stats in college and got little lost in topics where advanced Stats was used. To get better understanding of the concepts discussed in this book please search for "Statistical aspects of Data Mining" on You tube ,The lectures given in the aforementioned Google trainingcan serve as a companion to this book

Useful Book
This is a recommended book at Stanford University. Overall the book is very good in explaining data mining concepts in terms of simple applications and covers most of the major concepts. It covers the association analysis, clustering and classification techniques quite well.

However, this book does not go deep in some of the concepts in mathematical terms and the reasons some methods work and others don't. I wish it were more deep. The mathematical / statistical details could have been put at least in the appendix.

However, overall, it is a good reference book. I suggest combining this book with other data mining books.

No Validation to all those formulas...
I'm taking Data Mining as a grad course. I do not like this book. There are examples to math problems (which your professor will most likely assign you), and there are no examples in the book to go back to to validate your work on a certain type of question. I think it's a little old fashioned in that regard, as grad students nowadays do not want to spend all the time in data mining doing theoretical assignments. We need the practical tools too. Show me the data mining, then show me the theory.

Good overview (text) -- not actually useful to do any datamining
Pro:
Clear, easy to read text that covers many topics of data classification and mining (as described by the several other reviewers).

Con:
Don't expect to be able to actually perform any of the data mining techniques discussed in the book from (or while) reading it.There is no software that comes along with it, nor does the book champion a specific package that you can use while reading through the chapters.It kind of reminded me of the old Wendy's commercial "Where's the beef?"

Overall:
If you're interested in getting a general feel of what data mining has to offer, this is a decent first read.If want to do any of those things, you will need to seek out other sources.

I personally found books associated with specific software packages much more useful.Depending on your background, you may be better off skipping straight to them.
... Read more

Product Description
Globally, equity markets have experienced a rollercoaster of fortunes in recent years, particularly in resource and commodity prices. The Mining Valuation Handbook is the most comprehensive guide to mining valuation on the market. This third edition provides up-to-date information and unravels some of the mystery surrounding the resources industry.

The Mining Valuation Handbook provides mining information for the financial industry, and financial information for the mining industry. Readers will gain a greater understanding of:

• feasibility studies
• commodity values and forecasting
• classification of resources and reserves
• hedging commodities and exchange rates
• valuation and pricing techniques
• quantifying risk
• dealing with inflation
• share price performance
• commodity profiles.

Author Dr Victor Rudenno provides readers with valuable insight into the resources sector. The Mining Valuation Handbook is an essential addition to the libraries of astute investors and mining and financial analysts.

Customer Reviews (2)

Excellent book on Mining Valuation
This book is excellent.It's very easy to understand for a lay person and organized in a very systematic and step by step fashion.As someone moving from real estate investment in Russia into mining investment in Australia, I am certain that this book will be a constant resource to me in the future.It's a shame there aren't more books on investing that are so packed with information yet easy to follow for a novice.Definitely worth a read whether you are entering this field or simply looking for a way to understand a potential personal investment.Read it!

A good reference book for those interested in what drives the value of companies in the extractive industries
Despite a significant growth of interest in commodities recently there are not many books on valuation of companies in the extractive industries.Dr. Rudenno's book is a rather rare example of industry expertise well explained. A good reference book for those interested in what drives the value of companies in the extractive industries ... Read more

Product Description
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.

Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.

Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:
* A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.
* Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Complete classroom support for instructors at www.mkp.com/datamining2e companion site. ... Read more

Customer Reviews (30)

Excellent, fast shipping!
As wrote in title, it is really good book and fast shipping. I am satisfied!
Thank you!

Great book for data mining
I bought this book as a text book for data mining. I found this book give a solid introduction to multiple topics and a ready reference. One thing , I found though was a rather superficial treatment of very specific algorithms and a thorough treatment of general ones .Atleast the most popular specific algorithms can be detailed.

efficient, if technically a bit shallow
This is a useful book: it provides the most comprehensive state of the art overview of data-mining technology I know of.The emphasis is on 'overview' however - you can find starting points and intuitions, but you will not be able to to do anything very ambitious just on the basis of the purely technical information here.At one point, the details of how linear classifiers work are swept under the carpet with a faintly crass remark about 'fancy math tricks'.If linear classifiers are 'fancy math tricks', what does that make variational methods for probabilistic data modelling?Note, in fact, that advanced machine learning in general, where fancy math tricks are ubiquitous and unavoidable, is not touched - an interesting implicit distinction.

Further, this is not a book you are likely to read for pleasure, for either the prose or the presentation. If you are not professionally involved, you neither need nor want it.

Nevertheless, given all those reservations, I'm happy to have it on the shelf.

Good introduction on Data Mining
This book is a good introduction on Data Mining with solid explanations of the mathematics behind the methods.

Augustine Nsang: Data Mining Book Purchase
Very reliable seller! The book arrived in time and in very good condition. Thanks a lot! ... Read more

Product Description
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.

• Written "By Practitioners for Practitioners"


• Non-technical explanations build understanding without jargon and equations


• Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software


• Practical advice from successful real-world implementations


• Includes extensive case studies, examples, MS PowerPoint slides and datasets


• CD-DVD with valuable fully-working  90-day software included:  "Complete Data Miner - QC-Miner - Text Miner" bound with book

Customer Reviews (19)

Verbose, yet not very useful
This book is a 900 page ad for Statistica.

Highly verbose. Scattered trains of thought. These guys need to test this book on an audience, get some feedback, and hire a really good editor.

A library of data mining knowledge with good looking
This is a great book that I recommend everyone to have, from beginners to advanced data and/or business analysts. It covers a wide range of topics, presented in a rigorous but, at the same time, very interesting way. Most of the topics covered are augmented with some real life examples as well as good quality images which give the book a special flavour. This book is now part of my "often consulted books" shelf.

Just a book to promote software packages
I thought from its title that this book not only comes with real world applications but also introduces basic concepts and theory behind these data mining algorithms.I was not asking for in-depth explanation for these algorithms but at least, it has to be able to make us comfortable with these methods.However, after reading these algorithms covered in this book, I still have no idea how these algorithms work and what is the best way to approach different methods.Certainly a big dissapointment for me!

Adequate, but not spectacular; definitely for practitioners
This book is for practitioners, not for those seeking a deeper understanding of data mining.It both makes and delivers on that claim.All major data mining topics are covered, though in a manner that is shallow given the book's goal of getting past the theory and moving to the practice.

Oddly, the very start of the book does have a bit of theory in the form of the historical roots of statistics and the limitations of statistics that leads to the need for data mining; I found this bit of history quite fascinating and enlightening; it is something I've found in few other data mining books, and I've read several.

The trouble is, I do like theory a bit.I have a master's in computer science, so I'm a bit biased that way, thus my relatively low scoring of the work.

About 1/3rd of the book is dedicated to working through real problems, and that is the overwhelming strength of the book.If you are one who learns by doing rather than by theorizing, you'll find this book quite outstanding.

The biggest criticism I have of the book is that it is quite clear that there are significant parts where the authors just didn't have their hearts in it; it felt like they wrote certain sections because the publisher told them they had to in order to hit some type of target marketing segment.

It's also quite unfortunate that all three software products provided expire in 90 days or less.I'm never one to accomplish anything in 90 days, let alone get through a 700-page technical work!!!I know they are the 3 top mining tools, but I much prefer Oracle Data Miner, a product that is quite solid, never expires, and is free for non-commercial use.

Overall, a solid work.But to me, theory matters, that's one star down; and rigorous, enthusiastic writing matters, so that's two stars down.In the 3-stars that remain is lots of hands-on practice if you don't mind expiring software, and for that it is very strong.

Excellent book!!!
"On a side note, the book was excellent. It was informative and we found it to be an easy read, given the subject matter included. Thank you."



... Read more

Product Description
Data Mining for Business Intelligence, Second Edition uses real data and actual cases to illustrate the applicability of data mining (DM) intelligence in the development of successful business models. Featuring complimentary access to XLMiner®, the Microsoft Office Excel® add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of DM techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples, now doubled in number in the second edition, are provided to motivate learning and understanding. This book helps readers understand the beneficial relationship that can be established between DM and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions. New topics include detailed coverage of visualization (enhanced by Spotfire subroutines) and time series forecasting, among a host of other subject matter. ... Read more

Customer Reviews (11)

Nice summary of the major data mining technologies
This book hits a nice sweet spot between the highly technical descriptions of the algorithms aimed at grad students in Computer Science or Management Science and the superficial MBA-school overview of the techniques and how to interpret the results.Well written, easy to follow, and has lots of good examples.Comes with an Excel add-on that I haven't tried yet.

Good Response and quick
Book wss recived in good timing, and quick because i paid the quicker delivery payment. Just one thing is that the was damaged from one corner.

Great for its niche
This book is designed for business students who have already had a course or two in statistics and want a good introduction to statistical methods for data-mining.It has the right blend of concepts and methods presented in an intuitive way.It is the best book I've seen for its intended audience.

Useless
Pretty shallow presentation of very basic statistical algorithms. Algorithms presented without any explanation of mathematical backgrounds and assumptions when they can be used. Almost no formulas in text, just data and plots. These algorithms are standard statistical algorithms; it is not clear from the text whether "data mining" and "statistics" is the same or not.

What is worse, there is Excel library that must be used with the book. All examples are in the context of this library. LICENSE LASTS ONLY 6 MONTH. Means, after 6 month you can put quite expensive ($100) book in trash. Or spend few thousand bucks for full license, what taking into account the "sophistication" of the library would be wasting of money. Take R language that is free and infinitely more powerful.

Warning if you purchase used book: each copy of the book has unique license ID that must be used to download the software. Once downloaded, this software cannot be downoaded second time. This means that it is not possible to download software if you purchased used book

Practical hands-on introduction to data mining
This book is a great hands-on introduction to data mining. It comes bundled with XLMiner, an Excel add-in that implements popular classification and prediction methods. The chapter examples, problems, and datasets are interesting and challenging. I have used this book and accompanying material in an online course at statistics.com effectively for several years. ... Read more

Product Description
Understand how to use the new features of Microsoft SQL Server 2008 for data mining by using the tools in Data Mining with Microsoft SQL Server 2008, which will show you how to use the SQL Server Data Mining Toolset with Office 2007 to mine and analyze data. Explore each of the major data mining algorithms, including naive bayes, decision trees, time series, clustering, association rules, and neural networks. Learn more about topics like mining OLAP databases, data mining with SQL Server Integration Services 2008, and using Microsoft data mining to solve business analysis problems. ... Read more

Customer Reviews (8)

Data Mining with Microsoft SQL Server 2008
This is an excellent book for beginner to expert.Contains tons of relevant information.Could have been more detailed for the beginner, but authors did as well as possible without having to write a multi-volume version.Mid level to expert users should have no problem understanding the examples and solutions used in the book. I strongly recommend this book.

Data Mining with Microsoft SQL Server 2008
The number of stars may be unfair but for my purposes the book was not sufficiently elementary to get started. I have SQL Server 2008 loaded on my laptop with the Integration Services but I cannot tell from the tutorials that came with SQL Server how to get started building a data base and then using data mining techniques on it. The book mentions databases that can be downloaded but does not tell me where to put them or how to tell SQL Server where they are and connect to them. I'm familiar with the required knowledge about both data bases and statistics but not with SQL Server so this is not the book I needed. It may be very good but not for my purposes. I'll know better when I get going with SQL Server 2008. Peter Jurkat

Get your hands dirty with SQL Server 2008 Data Mining Tools
This is an excellent hands-on book on learning core concepts of data mining with SQL Server 2008. I have used this book as part of our Data Mining certification course at University Of Washington. The examples are very clear and their step-by-step approach really helped me in understanding various mining models and learning DMX query. Highly recommend for aspirant and experienced BI professionals.

A superb book and must have
Jamie MacLennan, ZhaoHui Tang, and Bogdan Crivat have done a superb job on this book and for those of us that had to deal with the 2005 version rest assured dear reader that the 2008 version is a 'work of art'. The writing is clear and concise. The examples are easy to work with, understandable and gone are the complex mathmathics that required genious to interpret. I have bought a copy for myself and two for the office. I have a problem with recommending books for the sake of recommending them. Trust me the 2008 version is worth every cent!!. Thanks Jamie et Al and well done!!!!

Great reference for REAL WORLD application of data mining
This is an excellent book, highly recommended by me and my numerous colleagues in the data mining community - one I support for a living, and not just teach.There is little use for theoretical learning if practical application is not also supported.Data Mining with SQL Server 2008 (as the previous 2005 edition) thoroughly covers not only the theory, but also the full feature-set of Microsoft's extensive data mining tools.Is it possible to buy a book with more detail on theoretical aspects of the subject?Possibly.But if you want to actually use the rich data mining features in the best business intelligence product available in the world both readily and efficiently without wading through stacks of PhD fodder, this book is perfect, and none can surpass it that I know of. ... Read more

Product Description
This one book outlines EVERYTHING a beginner will need and want to know about getting started at gold mining today, either as a hobby or as a small-scale commercial activity. In easy to understand language, supported by clear photographs and graphic demonstrations, this book covers all of the important subjects-including what gold is and looks like, where it comes from and where to find it, how gold deposits and how to find and recover it, and also touches on the legal aspects of how to claim the gold for yourself. The book covers up-to-date mining procedures of panning gold, sluicing, dredging, high banking, drywashing, electronic probing, hardrock mining, basic refining techniques, cleaning procedures, selling gold, and much, much more.Herein lies the most comprehensive and thorough work on electronic prospecting techniques (locating gold with metal detectors) available in a publication on the market today.Virtually an encyclopedia of modern gold mining techniques, there is no other book available more up to date, more simple to understand, or which covers the entire subject as thoroughly as this manual. ... Read more

Customer Reviews (6)

Not helpful

Doesn't cover the very important legal aspects of mining.

Nothing in it that you can't pick up in a few days of looking for gold.

After reading this book, you may need to monetize your gold mine!
If you are planning a trip out west and have questions about locating gold, this book is for you. It covers various subjects including dredging and sluicing just to mention a few. Who knows, you may end up being one of our clients who need to monetize their assets or take them into trade!

Larry G Potter
[...]

Dave Mack Does it all.
This has to be one of Dave's best books. It is right up to date and this is in 2008. Everything listed in this book is still where it is supposed to be. Bookmarks are good. This has to be the best book I have seen lately even his Detector section is still up to date. Now that takes some doing with the way electronics changes on an almost daily basis. So far nothing seems to be out of date.

Thanks Dave for a great read and such a great hobby.

dray

Gold Mining in the 21st Century
A good work,will reread much of it. Should be on the shelf as a reference for those interested in Gold.

Comprehensive and Encompassing
It would take a lot of research to find a better place to start, so why bother? This book answers all the questions you would have about getting started in the hobby of gold! ... Read more

Product Description
* Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems
* Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support
* The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining
* More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining
* Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis ... Read more

Customer Reviews (30)

Data Mining Techniques
Excellent coverage of various aspects of data mining.Popular as a textbook (reason for purchase).Plenty of graphics and illustrations; written in clear and easily understood English.

Data Mining book you should read first
Be careful, the first edition is MUCH older.Make sure you get the current 2004 edition.

There are most recent books, but this one is still worth reading first. This is especially true is you are an analyst. Managers of analysts might enjoy Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart or Competing on Analytics: The New Science of Winning, but analysts will need much more detail.

This the best single volume on Data Mining you can buy. As one who mostly teaches methodologies, I like that all the major topics are here: neural nets, market basket, cluster, and trees. But there are also techniques that SPSS and Clementine (the software packages I use) can not do like "link analysis". Also, unlike Larose Discovering Knowledge in Data: An Introduction to Data Mining, the data preparation reads like preparing data for data mining, not a carbon copy of preparing data for statistics. Regarding this issue see the excellent Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems). I have pretty much concluded that a data mining book that does not make clear that data mining and OLAP are not the same is not a great book.This book has an extended section on just that.It is highly readable and comprehensive.

Excellent book for Data Mining
As a novice to data mining, I was searching for a book that would explain the concepts, NOT mathematical formulas.This text provides the reader with a clear and comprehensive explanation of each concept, provided examples, and the readability is excellent.Who should read the book?Anyone in business -marketers to CEOs; and college students at all levels who are trying to understand data mining concepts.The book is not for mathematicians who are searching for algorithms.I would rate this book 5-stars.

Very Interesting book
I'm very interesting in Data Mining and i think that this book is a good introduction to this field. Thanks Amazon

A must-have book for your technical library
Anyone interested in automating and improving decisions should have this book. It is one of the classic works on data mining and well worth the read.
I really liked the book both because it is well written and because, although it drilled into a fair amount of detail about some of the techniques, it started each new section off at a high level. This allows someone without a statistical background, such as me, to read as far as I can in each section and then skip ahead to the next technique. This is a nice change from books that simply get more and more detailed as page follows page, preventing you from gaining an overview of the subject.
The book introduces data mining and a methodology for applying it, talks about some of the applications in "Marketing, Sales, and Customer Relationship Management" (as the subtitle puts it), walks through some statistical techniques and then spends the bulk of the book on various data mining techniques. It wraps up with a nice summary of how data mining plays with other technologies and with some practical advice on getting started.
One of the best summaries of where data mining fits is given early in the book where an enterprise is encouraged to:
- Notice what its customers are doing
- Remember what it and its customers have done over time
- Learn from what it has remembered
- Act on what if has learned to make customers more profitable
The authors point out that Data Mining is focused on the "Learn" stage or, as they put it data mining suggests but businesses decide.
The methodology section, and the subsequent notes that relate to applying these techniques in real life, talked about the feedback loops between steps in data mining - there is not a linear "waterfall" sequence of steps but constant iteration and learning. They also emphasized the importance of finding the right business problem at the beginning - start as someone once said, with the end in mind. This was reiterated when they quote Voltaire who said "Le mieux est l'ennemi du bien" ("The best is the enemy of good"). In other words, don't get hung up on trying to find the perfect algorithm, perfect answer. Instead build something that is good, that works, and learn and improve over time.
The authors made a big point out of the value of data mining for "mass intimacy", where you want to treat customers differently and there is a business reason to do so but where customers are too numerous to be assigned to staff. One of the issues they pointed out was that staff must be trained in customer interaction skills while also using all the data you have. The value of data mining in building a customer-centric organization cannot be overestimated. ... Read more

Product Description

This book provides a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered. The book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Internet support with lecture slides and project problems is available online.

Customer Reviews (3)

review from an academic who uses this book for teaching
So what does the author, Bing Liu know about Web data mining to write the book "Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data"[1] ? Fortunately the answer is "a lot!" This fact along with the title which had some cosine similarity with the names of my research lab and a graduate course that I have been teaching at the University of Louisville since 2004, and prior to that at the University of Memphis since 2000, are the reasons why I ordered a copy of this book. Bing Liu is a well seasoned researcher who has made significant contributions to association rule mining, in particular classification using association rule mining and association rule mining with multiple supports. He has also worked on Web data extraction, and more recently on opinion mining. In addition to the expertise of the author, two of the chapters, Chapter 8, Web Crawling, and Chapter 12, Web Usage Mining, were contributed by two leading experts in these respective areas, Filippo Menczer for the former and Bamshad Mobasher for the latter.
This book is appropriate for students at the graduate or senior undergraduate level, for practitioners in industry, and even as a good comprehensive reference for researchers in academia.
The Table of Contents held a surprise for someone who had always found it hard to limit the number of textbooks to one book in a web mining course that does not have data mining as prerequisite, and thus typically prescribes a good data mining book to introduce data mining techniques, in addition to a second book related to web mining. This book, on the other hand, has two parts, one devoted to data mining, and the other devoted to Web mining. While it was not a problem to find a very good data mining book (I have a few of them on my bookshelf), it was harder to find a book that addressed data mining and Web mining. It was also hard to find a good and comprehensive Web mining book, since most of them tend to focus on one or only two of the three main Web mining areas of Web structure, content, and usage mining (typically leaving Web usage mining in the dark, with just a small section, citing that it is an emerging area). This book, on the other hand, is a serious book on Web mining that also devotes a decent portion to data mining. I would describe the way the topics are presented as deep and rigorous enough in most chapters, which is in contrast to a large number of books on data mining and web mining. That said, because the book is full of simple examples that illustrate the methods being discussed, it is useful even for beginners, making it also appropriate for an introductory level course.

Good overview on current topics
What I liked most about the book was the scratch I got when facing all the possibilities regarding data that is free available on the Internet. My interest area is crawling, and there is an exclusive chapter about it on the book. But as with all others chapters, it's only a bird's-eye view on the topic, so specifically the crawler part of the book wasn't of much use. In spite of it, my expectations were reached with the rest of the work, since I just wanted to be aware of what is happening today concerning Web data mining. I must note that, although chapters on relevant topics are small (more or less 30 and so pages) and surely don't cover all the nuances, the book comes with plenty of references for anyone who wants to dig further.

Excellent graduate text and reference
This book makes a great text for graduate courses, as well as a reference for scholars.The chapters are well written and provide good examples for any significant concepts.Each section covers the basics to establish a foundation of understanding for someone unfamiliar with the area, but goes on to also touch upon the research forefront on each topic.One of the most useful sections I've found as a researcher is the Bibliographic Notes found at the end of each section which briefly describes the major groups of work within the topic with cites to major papers/articles/books in each of these areas (seems to be about 50 or so per chapter).

The only "drawback" to this book would be if you wanted to touch upon everything, there is far too much content for a single semester.However as mentioned above, the chapters are structured such that you could easily use the first couple sections of each chapter to cover all the foundations and either leave later sections for students to read on their own/select an advanced project, or cover the remainder in a 2nd semester.

I highly recommend this book to any graduate looking for a comprehensive text and reference on web mining.

(In the interest of full disclosure, I am listed in the acknowledgementsfrom providing feedback on a pre-print edition of the text that was used as our course textbook.I do not get royalties from sales in any way.) ... Read more

Product Description

The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining.

Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies:

1. Predicting algae blooms
2. Predicting stock market returns
3. Detecting fraudulent transactions
4. Classifying microarray samples

With these case studies, the author supplies all necessary steps, code, and data.

Web Resource
A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.

Product Description
Text Mining Application Programming teaches software developers how to mine the vast amounts of information available on the Web, internal networks, and desktop files and turn it into usable data. The book helps developers understand the problems associated with managing unstructured text, and explains how to build your own mining tools using standard statistical methods from information theory, artificial intelligence, and operations research. Each of the topics covered are thoroughly explained and then a practical implementation is provided. The book begins with a brief overview of text data, where it can be found, and the typical search engines and tools used to search and gather this text. It details how to build tools for extracting and using the text, and covers the mathematics behind many of the algorithms used in building these tools. From there you'll learn how to build tokens from text, construct indexes, and detect patterns in text. You'll also find methods to extract the names of people, places, and organizations from an email, a news article, or a Web page. The next portion of the book teaches you how to find information on the Web, the structure of the Web, and how to build spiders to crawl the Web. Text categorization is also described in the context of managing email. The final part of the book covers information monitoring, summarization, and a simple Question & Answer (Q&A) system. The code used in the book is written in Perl, but knowledge of Perl is not necessary to run the software. Developers with an intermediate level of experience with Perl can customize the software. Although the book is about programming, methods are explained with English-like pseudocode and the source code is provided on the CD-ROM.After reading this book, you'll be ready to tap into the bevy of information available online in ways you never thought possible. ... Read more

Customer Reviews (5)

Excellent!
This is a well written book, code is easy to download, and a number of topics.All in all though, the writing is clear and easily understood so it's well worth the money...

Good book to bootstrap yourself into Text Mining
I am a Java web/search programmer who wanted to "get into" text mining. I found this book an excellent resource for this. Text Mining is a field in which active research is still going on, and other Text Mining books I have looked at reflect this - the authors expect you to have a certain degree of mathematical background to understand what they are saying. This book explains briefly the math behind each of the approaches, but it focuses more on the algorithms that result from the math, so it is easier to read.

Of course, a side effect of this is that the approaches described are not necessarily the state of the art for solving any given problem, but once you get the basic approach to solving a problem, it is relatively easy to find and understand the documentation on the web for the more advanced approaches, since you now know what you are looking for and how it differs from your basic solution.

The book does have a (fairly long) chapter where it covers the math background necessary to get started with Text Mining. If you understand the stuff in there, you will actually be able to think up solutions to text mining problems that are unique to your own situation.

The algorithms in the book are in pseudo-code, but the book comes with a CD (or download from the author's sourceforge project textmine.sf.net) where you can see working Perl code.

Overall, I think this is one of the most useful books that I have purchased in a while. It should appeal most to programmer types who have programmed in their language(s) of choice for a while in areas other than text mining, wants to get into text mining, and doesn't want to spend a lot of time relearning high school and college math before starting off.

A Great Subject
Text mining is one of the most exciting subjects of the web, and too few books are dealing with it. This one is one of them, and it gives quite a few examples of text mining applications, like spam filters or search engine ranking algorithms. The style is easy to follow, and the concepts easy to understand given some maths background.

However, I expected more details, and a richer content overall, thus the four stars. This is still a good book.

An excellent guide to mining the Net
Software developers learn how to mine information on the Web and turn it into valuable data; but developers need to understand how data mining works. For a programmer's application-oriented review, Text Mining Application Programming is the item of choice: it reviews text data, how it's found, and how search engines locate and gather it. Next, it teaches how to build spiders to crawl the Web, how to use the information, and how to monitoring it. Perl developers will find its Perl-based code useful, but it's not necessary to know Perl to run the software herein. An excellent guide to mining the Net.

How to Find Information
There is an old expression that half of knowing anything is knowing where to find it. And there is little more frustrating to be looking at 'My Computer' trying to find what you know you have stored in a file somewhere. Well, perhaps just as frustrating is to go to one of the search engines and try to find something that you know is there but just don't know the proper words to find it.

In this book Dr. Konchady talks about how to go find data that is in text form on your system, on your network or out on the web somewhere. It talks about search engines, but also about other techniques that can be used only by programming.

The CD that comes with the book contains several Perl software snippets that help to find named entities, parts of speech, phrases and gives a summary of text documents. This area includes developing web crawlers that can be adapted by individual users to go out and find specialized information. It further contains an Open Source software package called Text Mine that is designed for mining operations. In addition it has utilities to build and enhance Text Mine and utilities to build and manage MySQL database tables. This is an excellent book on everything from the basic hints and types through some of the mathematics that underlies text mining.

His section on the nature of an English language Question and Answer system is the best I've ever seen. ... Read more

Product Description

Customer Reviews (4)

Too many loose ends
Valuing mining shares, which should be the meat of this book, is limited to Chapter 4, "Valuing Mining Shares," pp. 231-250.The treatment covers standard metrics for the most part, with few sector-specific insights.I would recommend that in the next edition the author devote far more space to working through comparative valuations and demonstrating the validity of the methods.Otherwise, the book is well written and an enjoyable survey of the mining sector.

Valuable book for mining investors
An Insider's Guide to the Mining Sector is an introduction to investing in mining shares. It was first published in 2004 and then republished in 2008. It gives a fairly complete overview of mining investments ranging from gold and silver to minor metals like tantalum and cobalt. Coal mining is also discussed.The various mining share exchanges such as Vancouver, New York, and London are described pointing out positives and negatives for investors. Special attention is given to trading gold company stocks. The author cautions investors not to fall in love with a mining stock which is on a roll (take your profits!) which is always sound advice. My only small quibble would be that the tables in the book, when discussing millions of tons of metals in reserve for example, list the reserves alphabetically by country and not by the size of the reserve. Overall it is updated and useful look at trading mining stocks.

Better books out there
Unfortunately my experience with this book is that it is only mediocre at best. While the book's cover claims to teach you how tomake money from mining shares, in reality it only barely scratches the surface on what you need to know to really evaluate mining shares.

For example drill results are a large part of the mining game and this book doesn't cover how to make rough estimates of how much mineral is in the ground based on those drill results. Not in any meaningful detail at least - there's perhaps a few paragraphs onthis, but nowhere near enough detail) Since probably 80% of all companies in the mining sector are non producers you're automatically excluded from 80% of the opportunities out there (unless you wish to take a major gamble and invest not knowing whether the set of drill results were great or not)

The book does howeverteach you how to value shares given forecast company cashflows using concepts such as the PE ratio, NPV and IRR, WACC and EVA. This is useful information, if you read mining share broker reports you'll know their valuations revolve mainly around PE's and NPVs. Still there's only a small chapter is dedicated to this, and I've found some free websites do a better job of explaining these concepts.

What the book does cover quite well is general knowledge in the mining industry - such as, what each commodity is used for, what the Poseidon and Bre-X sagas were about, and a bit about individual companies like Rio Tinto and Anglogold. Here again web resources here did the job equally well or better.

Recently I was looking at Rio Tinto's prospects just for interest. I found the USGS website to be a better source of data than the book for commodity profiles, and Rio Tinto's website a better soure of information on the company's operations.

In the end I'd suggest this book is okay for someone taking an idle interest in the resource sector, but if you're dead set on making money out of mining shares this book is just about completely useless. A much better one is Victor Rudenno's "The Mining Valuation Handbook" - which covers more practical topics such as how to estimate capital and operating costs for a particular mining operation, explanations of difference mining styles (e.g. block caving, stoping, etc), and generally things that affect the economics of a mining project, and for an investor that's the thing that really matters. If your company has just reported a blow out in their strip ratio Coulson's book just couldn't tell you to what degree this would affect company costs, or to what extent the lack of infrastructure would impact on the company's new mineral discovery. The book for me has been $50 very poorly spent.

Pretty good for investors w/o a technical background
I work in a financial firm that is very prominent in the junior to mid tier mining industry. For people without a very solid technical background--whether it be in geology or engineering--the entire mining industry and in particular, mining companies, can be very confusing to even begin to understand.

Investors need to selectively filter out the technicals -- let's face it, a typical investor would not have a PhD in engineering and geology and tens of years worth of experience to make a sound decision as to whether a particular mining company's technicals actually make sense.

Leave that to the technical people (and hope you don't hit another Bre-X). Investors need to get to the end result and I believe this book does a very good job in giving a general overview of how the industry works and how an investor can value the company.

Perhaps of my finance and slight accounting background, I find most of the financial terminologies in the book to be pretty straightforward. However, for people without a basic understanding of finance and/or accounting, this book could be more challenging than it's intended to be. ... Read more

Product Description

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

Customer Reviews (41)

Clear reference book
This is a great book for someone with already some background in statistics, but also for the complete novice in learning theory.

Standard textbook available online now
This book is one of the classics when it comes to the field of statistics and data mining. It provides a good mix of theory and practice in a concise manner - for statisticians and mathematicians at least.

The good teaching will make you understand the concepts of a huge variety of methods. Digging deeper you will probably need to consult a more specialized source for the particular method of interest.
Take a look at the table of contents for an overview.

The color print makes the book very visually appealing.

Note that the book can now just be downloaded!
[...]

Amazing Second Hand
This book itself is a classic for data mining. The one I got is a second hand, and it's in great condition. Shipping is much faster than I expected.

Useful research summary; a disaster otherwise
I have three texts in machine learning (Duda et. al, Bishop, and this one), and I can unequivocally say that, in my judgement, if you're looking to learn the key concepts of machine learning, this one is by far the worst of the three.Quite simply, it reads almost as a research monologue, only with less explanation and far less coherence.There's little/no attempt to demystify concepts to the newcomer, and the exposition is all over the map.There simply isn't a clear, coherent path that the authors set out to go on in writing a given chapter of this text; it's as if they tried to squeeze every bit of information of the most recent results into the chapter, with little regard to what such a decision might do to the overall readability of the text and the newcomer's understanding.To people who might disagree with me on this point, I'd recommend reading a chapter in Bishop's text and comparing it to similar content in this one, and I think you'll at least better appreciate my viewpoint, if not agree with it.

So you might be wondering, why do I even own the text given my opinion?Well, two reasons: (1) it cost 25 dollars through Springer and a contract they have with my university (definitely look into this before buying on Amazon!), and (2) if you actually already know the concepts, it is quite useful as a summary of what's out there.So to those who understand the basics of machine learning, and also have exposure to greedy algorithms, convex optimization, wavelets, and some other often-utilized methods in the text, this makes for a pretty good reference.

The authors are definitely very well-known researchers in the field, who in particular have written some good papers on a variety of machine learning topics (l1-norm penalized regression, analysis of boosting, to name just two), and thus this book naturally will attract some buzz.It may be very useful to someone like myself who is already familiar with much of what's in the book, or someone who is an expert in the field and just uses it as a quick reference.As a pedagogical tool, however, I think it's pretty much a disaster, and feel compelled to write this as to prevent the typical buyer -- who undoubtedly is buying it to learn and not to use as a reference -- from wasting a lot of money on the wrong text.

Interesting, a bit random, and perhaps misclassified
Very entertaining and in-depth review of the topic.But the topic is a lot of different things and there seems to be a bit of a mismatch between the content of the book, the title, and the Amazon categories it is given.Data mining, inference, and predeiction of course, probably have *something* to do with artificial life, but thats not the first thing a reader experts to read about for this kind of topic.

I did enjoy it but expectation management is key.It just ended up being about something a bit different than expected.

I was a good quantitative treatment of several different issues.It could have done a better job of explaining why that particular set of issues was a contiguous group of ideas.I could have imagined them talking about several different concepts as well.

The graphics are great.More stats books should spread their wings with some interest-keeping color. ... Read more

Product Description
The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing. ... Read more

Customer Reviews (17)

GREAT INTRO TO DATA MINING
I bought this book because I wanted a relatively high level (not too high level, but high level enough to give me a good foundation in the theory and issues) to data mining. My goal was to first understand the theory and principles of data mining before getting into the technological and application specifics (e.g., how to use software such as Dataminer or R or Weka or SPSS Clementine etc.).

This book has met my goals. Most chapters include abstract math/statistics that may be a little challenging for people who do not have a recent high level undergraduate statistics background. Actually I enjoyed the math/stats, and did not worry about going too deep into those portions. Trust me, the abstract concepts are not easy to grasp beyond a certain point, but they are EXTREMELY valuable. I am really glad that I was challeged. If you want another perspective or intro to data mining you may want to read some of the lecture notes of the "Machine Learning" course from MIT's online courseware - the courses are available for free on MIT's online courseware site. The lecture notes are even more abstract - they will make you appreciate this book.

I highly recommend that anyone who wants to get an intro to data mining should first read this book. After reading this book the reader can read a book that explains a specific data mining software package such as "Intro to R" or "Data Mining: Practical Machine Learning Tools & Techniques" (by Witten and Frank, good if you want to learn Weka).

finally a good statistical and computer science perspective on data mining
This book is not an introductory text. Anyone interested in a particular topic should consult the preface of the text to find out what it is about. The negative reviewers were not fair to the authors on that score. Had they read the preface they would have found out (1) how the authors define data mining, (2) that they see it as a subject with an important mix of statistical methodology and computer science and (3) that it is intended as an advanced undergraduate or first year graduate text on the topic.
They also provide a very well organized structure for the text that is well described in the preface. It consists of three parts. Chapter 1 is an essential introduction that is informative to everyone. Chapters 2 through 4 go through basic statistical ideas that statisticians would be very familiar with and others could view as a refresher. The authors have experience teaching this course to engineering and science majors and have found that many of these students unfortunately do not have the prerequisite statistical inference ideas and need this material covered in the course.

Chapters 5 through 8 cover the components of data mining algorithms and the remaining chapters deal with the details of the tasks and algorithms.

The book features a further reading section at the end of each chapter that provides a very nice guide to the useful and most significant relevant literature. The author's have done a very good job at this. One mistake I found was a reference to Miller (1980). I think this was intended to be a reference to the seocnd edition fo Rupert Miller's text "Simultaneous Statistical Inference" which was published in 1981 by Springer-Verlag but the full citation is missing from the list of references in the back of the book.

This book deserves 5 stars because it does what it intends to do. It presents the field of data mining in a clear way covering topics on classfication and kernel methods expertly. David Hand has published a great deal on these techniques including many fine books.

Mannila and Smyth bring to the text the computer science perspective. There is much useful material on optimization methods and computational complexity.

Statistical modeling and issues of the "curse of dimensionality" and the "overfitting problem" are key issues that this text emphasizes and expertly addresses.

The only thing the text misses is details on specific algorithms. But I do not grade them down for that because it was not their intention. They emphasize methodology and issues and that is the most critical thing a practitioner needs to know first before embarking on his own attack at mining data.

The text does provide most of the current important methods. Although Vapnik's work is mentioned and his two books are referenced there is very little discussion of support vector machines and the use of Vapnik-Chervonenkis classes and dimension in data mining. The new book by Hastie, Tibshirani and Friedman goes into much greater detail on specific algorithms include some only briefly discussed in this text (e.g. support vector machines). The support vector approach is also nicely treated in "Learning with Kernels" by Scholkopf and Smola.

I highly recommend this book for anyone interested in data mining. It is a great reference source and an eloquent text to remind you of the pitfalls of thoughtless mining or "data-dredging". It also has many nice practical examples and some interesting success stories on the application of data mining to specific problems.

make sure you are right audience
It's not that this is a bad book, but you have to make sure you are right audience.The book offers very high-level overviews on various techniques of data mining, but it is almost impossible to learn how to really implement them.Since there are no exercises after each chapter you probably already know who the target audience of the book are.

It shows me many examples
Even if it is bad as all the gentlemen said, I think at least it gives me many examples which are not mentioned in other books before.

Very, Bad Book !
I was very disappointed in this book. There are so many other books in the field of Data Mining that are so much better. This one has very little to offer.

It does a poor job explaining the theory.
It does a poor job giving practical "hands on" advice.

SAVE YOUR MONEY, AVOID THIS BOOK !!! ... Read more

Product Description
s and the pollution of earth, uncountable dollars worth of metals, fuels, and life-sustaining substances await in nearby space. In this book, noted planetary scientist John S. Lewis reveals that vast amounts of these important substances are locked away--for now--in the asteroids, comets, and planets of our own solar system. ... Read more

Customer Reviews (26)

Idea Book for Future Interplanetarians
This is not a cook book for serving up a turnkey interplanetary civilization, but its ideas will provide food for thought.

The book covers a broad range of subjects providing: historical perspectives; descriptions of the Moon, Mars, and the asteroids; technical processes for extracting/producing volatiles/metals; generating power; and spaceship propulsion schemes and flight trajectories.Of the ideas presented, two stand out as possible keys to the future ...

To ply the space between Earth, Moon, Mars, and the Asteroid Belt, you will need a spaceship, versatile in the propellants it can use.Rockets normally burn their propellants, but there is another type which simply heats them.Nuclear energy is the favored heat source.This idea has been around for years.The most accessible propellant in space: water.

Perhaps the best place to look for water is in a group of asteroids known as Near Earth Objects (NEOs).Their paths periodically cross the Earth's orbit.Some of these NEOs are suspected of harboring ice beneath their dark coats.The NEOs in an orbit similar to Earth's are easiest to reach.

Scenario:Your spaceship departs an Earth-orbiting fuel-depot.Months later, you intercept a NEO, mine its ice, possibly melting/purifying it before storing it.At departure, you can tap into this water to feed your thermal rocket.After more months, you arrive back at the fuel-depot.The water you add to their stores can be used for flights to other destinations.

NEO mining could be dangerous.NEOs spin, have low/variable gravities, some may be a collection of loose rocks, some are two smaller bodies sitting on each other, some have small moons, and some are has-been comets. What will happen when you start boring, digging, or blasting them?

Book quality: page 79 follows page 82.

Non Fiction
Mining the Sky : Untold Riches from the Asteroids, Comets, and Planets
by John S. Lewis takes a balance looked at the possibilities and/or necessities of space exploration and exploitation for economic reasons.

There are a lot of resources out there, and finite resources here, and he looks at both private and public involvement in the activity.

Now I see how it can be done
A short way into this book, I went to the back of the book to see if the author is a journalist or a real scientist. That's because it was so well written. He's a scientist alright. And, it wasn't long before I encountered the dense exposition I expected.

So, there's a dusting of light reading, especially the scifi scenes that serve as introductions to each chapter. The craftsmanship of those would make a professional scifi writer envious.

Then there's the info-packed core of each chapter. My chemistry and astrophysics is practically non-existent and I couldn't keep up, but I got the gist of it. I still appreciated the effort to explain things.Other authors would skip the explanation and merely state the conclusion. That would leave me wondering how trustworthy that statement was.

In the end, I felt I had a good overview of how the future might take shape.

I should warn you of that, at the start of the book, the author presents a version of 15th century Chinese explorations (he doesn't mention the name 'Zheng He') that is a little shakey historically. But blaming "the court eunuchs" makes too good a metaphor to let that get in the way. However, for a couple chapters at the end of the book he turns preachy -- essentially labelling dissenters from expansion into space as "court eunuchs", then disassociating himself from the political left and right by sloppily redefining their positions. I guess he couldn't trust us to make our own way thru political thickets. Fortunately, the just-the-facts bulk of the book make up for these few tantrums.

Amazing and important book, even 10 years later
This is a wonderful book.The author lays out, very plainly, how the vast resources of the solar system will enable a prosperous future for 10 quadrillion people within half a millenium, and at the same time save the Earth from the economic and ecological dangers it now faces.

Parts of the book are a bit dated now, including the "new afterword by the author" which was written in 1997 (only a year after the book was first published).I'd love to see a new edition that takes into account the developments (or lack thereof) of the last ten years.But the vast majority of the book still applies just fine.I highly recommend this book to anyone with any concern about humanity's future.

This needs to be required reading in schools
Mining the sky is an encouraging answer to those who worry about overpopulation, global warming, and environmental degradation.It challenges us to expand our limited perspective and seek solutions to the worlds problems in unconventional places.Lewis very logically and reasonably explores the potential wealth of our solar system, and lays out a very feasable framework to follow in order to utilize the seeminly unlimmited resources in our backyard. ... Read more

Product Description
Text mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities. ... Read more

Customer Reviews (4)

New material but not really applicable
I was very disappointed by this book. It has some good material but examples are really missing. It might be a good book for technical managers to have a high level picture of the field. As a graduate student I don't seem to be able to use this book in any practical project.

Ok overview but not sufficient
This book is a decent overview of the field but I don't think it will prepare anyone to actually create text mining systems. Feels like its oriented totechnical managers rather than the people who will be actually building the stuff. Examples are really lagging, the very little psuedo code that does exists is not that good. It has very good definitions of concepts and explains what types of algorithms you'll be using but its very much like an encyclopedia. After reading through this book i quickly purchased Konchandy's Text Mining book to see if it was better. I haven't finished it yet but so far its great, would recommend that book over this one if you actually have to build text mining systems. On the up side, this book has very comprehensive references to find more information on each section which could prove very useful.

Great overview
This was one of the few books that included a very clear and extensive treatment of information extraction techniques. There were plenty of diagrams which is great for a visual learner. All the techniques are explained using both plain English and formulas, so that you can pick up the scientific notation with minimal previous knowledge.

Even when the authors plug their own company and research at the end it was moderately useful in illustrating the concepts mentioned in a real world scenario.

For Advanced Undergrads to Practitioners
The amount of textual information floating around on the web is staggering. And the overwhelming amount of this information is simply passed along from sender to receiver for the human being reading it to make sense out of it. There are obvious demands to use a computer to 'read' this material and select out appropriate nuggets from the ore.

Intended for use by advanced undergraduate students, graduate students, researchers and people working in the field, this book first covers the definition of the problem and presents several state-of-the-art probabilistic models for information extraction, and how these models can be used in applications. Finally a rather detailed description of several real life applications are included: patent searching, scanning magazine articles, and scanning the news for business intelligence.

Do you suppose that somewhere, some body (or maybe a lot of bodies) are using these techniques to find words like bomb, explosion, etc. ... Read more

Product Description
Economic skill is an essential partner to technical skill in every step of the mining process. An economic "mindset" begins before the first drill hole. This new book will help you effectively direct mining operations through the use of innovative economic strategies.

The text covers what is meant by a cost-effective mining scheme, the economics of information, and the procedures for rational evaluation of uncertain projects. It defines "ore" from an economic perspective and covers the influence of scheduling on ore reserves.

Discounted cash flow techniques, the most widely used evaluation technique for investment decision making, is covered in detail. The assumption of the use of spreadsheets is unique to this book. The application of DCF techniques in an operating mine environment is given expanded coverage and examples are drawn from real-life studies.

The differences between economic decision-making--a forward-looking task--and the reporting of results via accounting methods--a backward-looking activity are reviewed. Capital and decision-making procedures associated with capital investments in a risk environment are given extensive coverage. Case studies for capital investment in an operating mine are included. Comprehensive examples investigate "value" from a risk-reduction perspective and from an "expected return on investment" perspective.

This book offers solutions to the problem that many mining projects fail to achieve expectations because of their inability to adapt to change. A new technique is explained that allows calculation of capital that is "at risk" from capital that is not at risk. This promises significant advances in the way that investments are made and capital is valued in the industry.

The book concludes with a brief review of the historical setting and knowledge difficulties in any mining-related investment, and how these issues might also influence the success of investments in the future. ... Read more

Customer Reviews (1)

Concise, Comprehensive and Understandable
If you're unfamiliar with economics in general, but are in the mining industry, I would recommend this book to you.Not only is Mining Economics and Strategy easy to understand, it incorporates helpful examples, methods and programs to support the text.

The book is not limited to mining engineers either.Examples and all information can be understood and utilised in all economical situations - i.e. anywhere money is involved.

As a mineral processing engineer this book was recommended by a mining engineer and I was pleasantly suprised with the detail and presentation of the book ... Read more

Product Description
Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions

In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use. ... Read more

Customer Reviews (14)

Good, easy-to-read book, but lacks some best practice features
Good book for learning about the data mining techniques of logistic and linear regression. It helped highlight some good uses, and fortunately, I've recently had the opportunity to use it in my work.
However, I was a bit disappointed that the data preparation seemed very coding intensive. The author could have shown readers how to merge lookup tables of risk values onto customer datasets, rather than hard-coding each of the rules and values; or to use the SAS procedure for creating indicator variables, instead of writing the rules for each category.
Overall, I'm glad that I purchased the book - it lives up to its claims - but it misses some of the better practices, and time saving devices, in data preparation

nice book
Very nice book with a lot of SAS code. It is very helpful for the statistician who wants to enter the business area.

Practical and Powerful
This book is really a useful step by step guidance to build a model using logistic regression. It is very practical and to the point. This book covers the business envrionment from high level and go down to the working data level and then again relate how the results from mining the data can solve busines problem.It is a treasure for data mining analyst and modelers.
Just as the author point out, although there are many new model building techniques emerge every year, logistic regression still remains a very powerful data mining and model building tool.And it is well demonstrated in her detailed examples.

The True Data Mining Cookbook
In the Data Mining field, this book is the most clear and concise and well-organized book for many years.This book truly deserves to be called the Data Mining Cookbook because it appeals to everyone interested in the subject.In other words, her writing style appeals to both the non-statistician and the statistician.The theory is well explained for the general public.She gives the kind of details that allows anyone with a college education and who is determined to be able to do some of this analysis on their own or at least supervise someone who is doing it for them.

Predictive Modeling Methodology For The Non-Statistical!
Logistic Regression From A - Z!This book has it all.

The author lays out clear, concise methodologies to build robust predictive models using SAS.The nice thing is this book lays out the process step by step with SAS code examples.You do not have to be a statistics major to understand how to use the built in SAS functionality.

The modeling methods are unbelievably detailed including topics like defining the objective function, testing variables for predictability using chi squared, fitting continuous variables using the most linear variable transformation format (squared, cubed, cubed root, log, exponent, tangent, sine, cosine, etc... 19 total formats),changing categorical variables to continuous indicator variables for logistic regression use, using stepwise, backward, and score regression methods to further eliminate less predictive variables, defining deciles, and model testing methods like bootstrapping, jackknifing and gains tables to validate the model.

I do not fully understand the mathematical concepts involved throughout the entire process nor do I want to.The book provides a consistent repeatable programming methodology to follow that is broken down into very quantifiable steps.

I would recommend this book for anyone with limited statistical knowledge and a need to understand predictive modeling programming methodologies.Knowledge of the SAS programming language is essential to make full use of this material.The book uses real life examples from the banking, insurance, and marketing industries and contains additional valuable information related to these fields. ... Read more

Product Description