Book 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 miningincluding 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 visualizationin a new, interactive interfaceDownload Description
Like the popular first edition, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining-including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction, including a fully-revised Chapter 8 and 30 new technique sections, is a fully functional platform-independent Java software s ... Read more

Customer Reviews (21)

Awesome
I am very happy with amazon purchases as they always come quick, as described. I love the free supersavings shipping program. Prices are chargedin the middle (not the chepest, not the highest) but I know I can always rely on Amazon! Every time I have something to buy online, I go to Amazon.

Good textbook for data mining and machine learning, but heavy at times
I'm taking a machine learning class and we use this as a textbook. It explains all the concepts clearly, but is sometimes a bit hard to digest through and goes at a fast pace. We use supporting material and lectures to make full sense of the material, I'd be a bit lost without the external help.

Oustanding broad overview, a pleasure to read
For those desiring an approachable and broad introduction of the machine learning, this book is an excellent selection.The fundamental concepts are explained with an applied perspective in a clear and concise language.Several sections are also devoted to exercises with the java Weka library, giving the reader plenty of opportunity to appreciate the lessons learned and get their hands dirty with a few examples.This edition compared to the previous edition has greater coverage of the exercises.I highly recommend this book.

Customer Satisfaction
The book I got was in very good condition and the time it took for delivery was also good

A great introduction to data mining and machine learning
I bought this book in the hopes that it would help me better explore the data from the Netflix Prize contest, which it did. I had been reading numerous Wikipedia articles, scientific papers, etc. on line and felt it would be useful to have a more general tome on the subject. This book covers many of the common, overarching themes i.e. clustering, neural networks, linear regression, etc. to varing degree. I only wish the examples involved slightly more complex data sets and more pseudo code was provided. I suppose since the book is very closely tied to WEKA, one could always dig through the source code of that application; but I feel that the authors could have provided a bit more of the strictly algorithm relevant code in the book. ... Read more

Book Description
Your in-depth guide to using the new Microsoft data mining standard to solve today's business problems

Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use. Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends. They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects.

You'll learn:

• The principal concepts of data mining
• How to work with the data mining algorithms included in SQL Server data mining
• How to use DMX-the data mining query language
• The XML for Analysis API
• The architecture of the SQL Server 2005 data mining component
• How to extend the SQL Server 2005 data mining platform by plugging in your own algorithms
• How to implement a data mining project using SQL Server Integration Services
• How to mine an OLAP cube
• How to build an online retail site with cross-selling features
• How to access SQL Server 2005 data mining features programmatically

Customer Reviews (13)

Just what I needed!
I have only recently started to get involved with Data Mining.I have been doing back end work with Analysis Services for a couple of years and we're ready to move on to the next level.

This book was amazing!The background in Analysis Services and Databases helped a lot, but the book covered all the topics in an easy to understand order.Sure, the chapters on the different algorithms can be very in depth, but apart from explaining the actual mathematical formulas, there is a huge amount of information about each algorithm that each developer MUST use when designing a Data Mining solution.

One of the reviewers commented that they can still not write a DMX statement.I'm confused by that statement!I am writing DMX statements using only the information I got out of this book.Sure, there are a few spelling mistakes here and there, but using the sections in the book where the syntax is fine, I've managed to run all the queries without issues.

I highly recommend this book.

Great Book for All Levels
I was really impressed with this book. It had a great introduction to demystify the typic of data mining. Since the learning curve on this topic is so high, these first few chapters are essential. It then immediately jumps into a practical example to help the reader bring it all together. The chapters get progressivily more difficult through the book and there's a chapter for each of the algorithms. The author team did a fantastic job and I'd highly recommend it.

A bible for those using SQL 2005
Some people commented on the poor editing: typos & some wrong pictures. True. (It detracts, but you can figure it out easily)
Some stated that it is not a good general overview of Data Mining. True (though it has a bit of a summary)
Some stated that is doesn't discuss business applications in detail. Yes, (it only makes brief reference to them).
Some stated that it is very vendor specific. Hello, read the title - SQL 2005.

It is a must read for anyone who wants to take maximum advantage of SQL Server 2005 Data Mining. It goes thru all the algorithms, tells you how each one works, how to tune them & how to embed them into your applications. It compliments the Books On-Line materials, tutorials & sample code that ship with the product.
(interesting how people pay for a textbook & never bother to read the copous amount of materials that ships with the product.)

It does give you a bit of background in DM, & does walk you thru using the tools (SSMS & BIDS) used to create & administer the Data Mining.
It doesn't talk about using the Data Mining Viewer controls in Visual Studio 2005.

It is an easy read & very informative. Especially if you go to the trouble of downloading the samples & data from the web site & actually build the models with the book & step thru the code.

While it isn't really an indepth treatment of DMX in the way that "George Spofford's MDX Solutions" is for MDX. It does give you more than enough examples to be able to create, train & predict from the models.

It also gives enough to embed your DM models into your applications, Use them from Excel & take full advantage of the DM built-into SQL Intergration Services.

If you want an DM Overview for business use - check out Barry Lindof's book

Helpful to a Point
I BOUGHT THIS BOOK HOPING IT WOULD GIVE ME SOME INSIGHT AND BACKGROUND TO BEGIN USING 2005, AND IT DID JUST THAT. SEEMS TO GO INTO THE "MATH" A BIT TOO MUCH, AND MOST WAS OVER MY HEAD. COULD HAVE USED A BIT MORE ABOUT THE MMC AND HOW TO USE THAT TO BETTER USE, BUT ALL IN ALL A BOOK THAT IS GOOD FOR MY LIBRRAY. F ANYONE KNOWS OF A GOOD RESOURCE ON USING THE MMC IN SQL2K5 LET ME KNOW:-)

Decent Book
At the time I'm writing this review this book is the only one available dedicated solely to the data mining features of SQL Server 2005. The book is good, but I was disappointed in it as well on three fronts. First, there is a chapter dedicated to each of the data mining algorithms. I didn't find the business use case examples for when, why, and how to use each algorithm sufficient. Second, each of the algorithm chapters goes off the deep-end explaining the mathematical basis for the algorithm. There are very, very few developers who are going to remember enough of their college mathematics to follow along. Third, the technical coverage of how to use the APIs and the data mining extension language (DMX) is superficial, particularly with DMX. After reading this book cover to cover I couldn't go off and write a DMX query if I wanted to. On the application I'm working on we are planning to implement our own web visualization viewers for the data mining algorithms. This book didn't give me what I needed in understanding the object model exposed by the APIs in order to handle the back-end coding for this. All in all, if you are planning to do data mining with SQL Server 2005 I would recommend this book only because at the time of this writing there is nothing else available. However, you will learn quite a bit about data mining with it and depending on your prior experience (more is better) it might be an excellent fit for you. ... Read more

Book 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 (24)

Good high-level review with little mathematics.
This is a great textbook for an undergraduate or layperson to the information sciences, but specialists may find it lacking depth. It is very good at identifying practices and principles that would guide a high-level planner toward a sound research program. That said, this book exhaustively covers the breadth of the modern field at the expense of formulas, algorithms, and source code that would have been valuable to an engineer or scientist with plans to implement.

* Buy this book if you require a high-level understanding of the concepts and techniques used in the field.
* Don't buy this book if you are planning to specialize in data mining, or if you have plans to implement yourself.

Significant improvements since first edition
I have read the first edition of this book years before. This second edition has significant improvements. Core topic (classification, clustering, association rules) is very detailed and much easier to read. The author also add much material about advanced topics such as graph mining, multimedia mining, stream and time series mining, etc. Although these advanced topics are not as well writen as core topics, at least you will get idea about what's going on in these areas.

1 star is too much for this book
Do yourself a favor and stay away from this book.The book is put together by gathering some data mining concepts padded with tons of buzzwords to "teach" you about data mining.It really fails to teach you anything and it really succeeds to confuse the hell out of the reader.The language used on this book is the poorest I have ever seen in my entire life.The people how wrote this book and the publishers who published it should be ashamed of themselves.It is a shame that I had to pay for this piece of work because I have to read it for a course I am taking and I really dread opening it every week.

One of the Best Data Mining Books
This book is easily one of the best on data mining.The one flaw I see is the meager attention given to neural networks.Coverage is practical, not theoretical.

Best introduction I know
It is very easy to collect huge volumes of data - social statistics, bank records, biological data, and more - but very hard to pull useful facts out of the heap. This book is about processing large volumes of data in ways that let simple descriptions emerge.

This is an introductory level book, aimed at someone with reasonably good programming skills. A little facility with statistics might help, but certainly isn't necessary. The book starts gently, with some very basic questions: what is data mining exactly, when there seem to be so many definitions for the term? What is a data warehouse, and how does it differ from a database? Next, the authors address the data itself in terms of quality, usability, and organization for efficient access. The central chapters, 4 thhrough 8, address various kinds of query specification, kinds of relationships to extract, correlations, clustering, and classification. None of the discussions is especially deep. All, however, are presented in pseudocode or simple math that can easily be translated into working code. The careful reader learns a few basic principles that work well in many contexts: entropy maximization, Bayesian analysis, and simple stats. It may be surprising to see how little of normal statistical analysis is used. I suspect the authors assume that stats-savvy readers will already know how to apply significance testing, and that stats-naive readers don't need the distraction. The last chapters discuss complex data, where the best structure for the data and the questions to be asked of it are not at all obvious, and tools and applications used in data mining.

The book is nicely laid out as a textbook, with an orderly summary, problem set, and bibliography at the end of each chapter. The bibliography is more than just a list of names and authors - it actually helps the reader decide which references will give the best description of each of the chapter's topics.

This is a clear, usable introduction to data mining: the data it uses, the questions it answers, and the techniques for connecting them. It gives codable detail for lots of techniques, and prepares the reader for more advanced discussions. I recommend it very highly.

//wiredweird ... Read more

Book 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

* Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis ... Read more

Customer Reviews (28)

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.

Excellent introduction
This well-written book is an excellent introduction to the data mining and predictive analytics space.The reader should be comfortable with data and data analysis.The reader, however, does not need any pre-existing knowledge specific to data mining and predictive analytics.Much of the book, including the middle chapters which describe specific analytic techniques, has general applicability to business problems beyond CRM.

I am an actuary working in the insurance industry and am ordering my second copy of the book.

Practical examples not convincing, lack of benchmarking
While the book is easy to read and not too technical, the applications investigated by the authors are too simplistic and not really convincing as to why we should use advanced techniques. It would have been nice to add an additional, more detailed chapter comparing the various implementations of data mining techniques by software companies (SAS Entreprise Miner, Clementine, Insightfull Miner, etc.) ... Read more

Book 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 (7)

Amazingly well written: simple, to the point, easy to read, and full useful information
This book is amazingly well written. Everything is explained in a very clear and to-the-point style. The book can be read from front to back or used as a reference book. It contains countless diagrams and the structure of the content is immediately apparent.

The book covers a lot of the important aspects of data mining. It provides algorithms and techniques for classification, clustering, association analysis, and anomaly detection. Every algorithm is not only formally stated, but also explained in a way that conveys intuition.

I only wish other authors also wrote books this way.

More than just about data mining
This book gives an excellent overview of data mining techniques, and gives thorough information about machine learning fundamentals.The key advantages of this book are its clean structure and high quality content and illustrations.

Great Introductory Text
I've just made it through the first 6 chapters of the book so far but I really enjoy this book so far.This book is terrific at introducing this material in an easy-to-understand manner.I've found myself using to supplement my machine learning textbook when more thorough explanations are needed.The section on support vectors was the easiest to grasp from about a dozen references I had on hand.I've seen a few typos here and there but I suppose that's expected from a first edition.

Data mining book focusing on clustering
I decided to start with this book as I think it is the most convenient to start in the data mining field. One big advantage of the book is the way data mining techniques are explained. It is mainly based on textual and graphical explanations. There is little equations, only what is necessary to implement the algorithms.

This book widely cover areas such as data preparation and understanding, classification, anomaly detection, association analysis and clusering. Although the book has a strong emphasis on the two last ones, nearly all standard data mining techniques are at least briefly discussed. However, this book does only have a fiew pages about kernel methods for example. Indeed, it is normal, as kernel methods are more suitable for machine learning (I mean making prediction) than data mining (I mean looking for description).

Therefore, this book is:

* able to explain data mining without thousands of equations
* a good way to start with data mining
* covering nearly all standard data mining techniques
* focused on association analysis and clustering

and it is not:

* a good book for kernel methods and other advanced techniques
* written in the statistical nor in the database perspective

My comment: if you are in the data mining field and not comming from mathematics or databases, then you really should buy this book.

Good overview, but needs to include real-world case studies
Data mining could be considered to be "Artificial Intelligence Lite", since it deals with many of the same issues in learning, classification, and analysis as they occur in the field of artificial intelligence but does not have as its goal the construction of "thinking machines." Instead, the emphasis is on practical problems that are important in business and industry, even though the solutions of many of these problems makes use of techniques that a thinking machine should be expected to have. Data mining has become an enormous industry, and has even been the subject of political and legal concerns due to the efforts of some governments to mine data on its citizens. This book gives a general overview of data mining with emphasis on classification and associative analysis. Anyone who is interested in data mining could read the book, but some rather sophisticated background in mathematics will be needed to read some of the sections. Pseudocode is given throughout the book to illustrate the different data mining algorithms. There are also exercises at the end of each chapter, but noticeably missing in the book is the inclusion of real case studies in data mining. The inclusion of these case studies would alert the reader to the fact that data mining is of great interest from the standpoint of business and industry, and would lessen the belief that data mining is just another academic field or just another branch of statistics.

Speaking somewhat loosely, the goal of data mining is to find interesting patterns in massive amounts of data or the classification of such patterns. This entails of course that one have a notion of what is "interesting" and one ofthe main problems in data mining is to find suitable `interestingness measures'. And since one is typically dealing with large amounts of data, one must use various statistical sampling and preprocessing techniques to massage the data and obtain a `representative' sample of the original data. In addition, one must be able to handle data that is `anomalous', i.e. data that has characteristics that are markedly different from most of the other data, or that has attributes that are unusual if compared with typical values for those attributes. These issues and techniques are discussed in detail in the first three chapters of the book, where the authors outline some of the bread-and-butter topics needed for effective manipulation of data.

The real substance and power of data mining comes from its role in classification and for discovering interesting patterns in huge data sets. The authors, in chapters 4 - 7, discuss various powerful techniques for data classification and association analysis. Association analysis in particular has been used quite extensively in recent years, due to the use of market basket transactions in on-line purchasing and the goal of marketers to learn the purchasing behavior of their customers. Association analysis uncovers relationships in the marketing data in the form of `association rules'. For disjoint itemsets X and Y, an association rule is a logical implication expression between these itemsets that has a certain `strength' that is measured by its `support' and `confidence.' The support measures how often a rule is applicable to a given data set, while the confidence measures how frequently the items in Y appear in X. The support reflects the ability of the rule to be not due to chance alone, while the confidence measures the reliability of the rule inference. The collection of all association rules that can be formed from a data set is too large to be practical and so strategies must be developed to prune the number of rules. The authors discuss in detail various methods for dealing with this computational drawback, such as `frequent itemset generation' and `rule generation.'

The detection of anomalies consists of the identification of `outliers', which as the name implies are data objects that lie "far away" from the other data objects. It remains of course to quantity what it means to be "far away" and for this reason this branch of data mining, as the author points out, is sometimes called `deviation detection' or `exception mining'. The omission of outliers is sometimes justified, since they are merely artifacts that only serve to alter the statistics of a particular data set. However, sometimes their presence signals important information, if not a major scientific discovery. Data mining therefore must contain tools that detect anomalies intelligently and efficiently. The authors discuss anomaly detection in fair detail, emphasizing the statistical techniques that are available to do it. They classify the techniques for anomaly detection as being `unsupervised', `supervised', and `semi-supervised'. As the name implies, supervised anomaly detection requires the existence of a training set with both anomalous and "normal" data with each class being labeled as such. When these labels are unavailable, one has to perform unsupervised anomaly detection, and for this approach to work the anomalies must be distinct from one another. If the normal data is labeled but the anomalies are not, one must do semi-supervised anomaly detection. The only weakness in the authors' discussion is that they do not include real-world case studies that illustrate the different techniques, such as clustering and density methods.
... Read more

Book 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 (2)

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

Book Description

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques.

Book Description
While we worry over the depletion of the earth's natural resources, the pollution of our planet, and the challenges presented by the earth's growing population, billions of dollars worth of metals, fuels, and life-sustaining substances await us in nearby space. In this visionary book, noted planetary scientist John S. Lewis explains how we can mine these precious metals from the asteroids, comets, and planets in our own solar system for use in space construction projects. And this is just one of the possibilities. Join John S. Lewis as he contemplates milking the moons of Mars for water and hollowing out asteroids for space-bound homesteaders-all while demonstrating the economic and technical feasibility of plans that were once considered pure fiction ... Read more

Customer Reviews (25)

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-existant 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.

Makes One Think
Mining the Sky is an excellent book for any person who has had any association with earthly mineral extraction and the potential for utilization of space-based resources on the Moon, Mars, and Near-Earth Asteroids (NEAs). While the book is nearly a decade old, the primary message remains poignant and relevant even more so in the 21st Century. It is my hope that the author will do a second edition in the near future. With recent robotic missions to the Moon, Mars, asteroids and comets having taken place since the book was first published, I am certain that there is even much more to now be said about the economics of space-based mineral commerce. ... Read more

Book Description
Much more than a fee mining guide, this unique book is a treasure trove of interesting sites to see and things to do related to rocks & minerals, gemstones, crystals, fossils, gold and other treasures. Whether you are an experienced rockhound or prospector, or a family on vacation, you'll find fun, adventure and maybe precious gems, gold or other hidden treasures of man or nature. Organized by state, 270 sites are listed in 31 states throughout the northeast, southeast, and mid-west, including 75 fee digging sites, museums, cave and cavern adventures, tours and historic and natural points of interest. Local history, regional attractions and camping information are included with times of operation, adress and contact information, cost, tools and supplies needed. ... Read more

Customer Reviews (1)

This is a great book
I used the other book ( fee mining in the west ) on my last vacation.The books in this series are great.I love them.If you like to travel around and collect neat looking rocks, gems and minerals like we do, get these books !The books give such good directions that I was able to locate a mine that had been closed due to the owners death.All evidence of the mine was removed but we still found it from the books great directions.Buy these books they are great ! ... Read more

Book Description
Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence


In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models.

Featuring 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 data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.

Data Mining for Business Intelligence:
* Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis
* Features a business decision-making context for these key methods
* Illustrates the application and interpretation of these methods using real business cases and data

This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions. ... Read more

Customer Reviews (4)

Excellent MBA/B-School Data Mining Book
I've used this as textbook for three years (even before it was in print) for my "Business Intelligence Using Data Mining" elective MBA course at the Indian School of Business. Till last Fall, I used to structure my class around the four major data-mining techniques explained well in this book; classification, prediction, clustering and association rules (what goes with what). The last time I switched completely to driving the class using the six or seven excellent cases at the back of the book, and the Business students loved that.

The cases and the associated data are rich; providing a business context to anchor the learning for students in the B-School. They allow the instructor to naturally cover important practical issues, such as over-sampling (when events that one is interested in -- say load defaults -- are rare), and asymmetric classification costs.

My class typically has a group project, where students have to pull everything together, from identifying a data mining opportunity, to collecting the data (beg, borrow or crawl:-), to performing exploratory data analysis (a key chapter in the book), to analyzing and presenting the results. Its usually more work than the students expect, but also typically much more learning than they expect.

In summary, a great resource for teaching the principles of data mining to anyone, and particularly useful for those in a Business School setting.

An Excellent Introduction, Works with Excel
Data mining is the extraction of useful information from large amounts of data. Perhaps the best example of this is Amazon. If you go to Amazon to look at a book, you'll find such tidbits of information as a section on the page headlined 'Customers who bought this item also bought' and another 'What do customers ultimately buy after viewing this item?'

That's datamining, dozens or hundreds, or thousands of people looked at the page about this item. Then they went on to take these other actions. Among all the data that Amazon has collected they mine their database and pull out information to fill in these blocks.

This book, intended for MBA level students gives an excellent introduction to data mining. It further includes access to an Excel add-in called XLMiner that is specifically set up to allow the student to use Excel to learn how data mining is done.

The one thing I would ask the authors to do in their next edition is to provide a brief review of the commercially available data mining software products that are available. If not all of the software, perhaps just the top half dozen or so. In real life we aren't going to use Excel for data mining, our data resides in a database somewhere.

Condensed Discussion of DataMining
This book discusses some of thetechniques used
in Data Mining.
It goes into Data Exploration as well as Evaluating
Classification and Predictive Performance.

Some of the more advanced techniques such as
Neural Nets and Cluster Analysis are
also discussed.

To learn more about database design and relational data modeling visit
[...]

From the authors:
This book got its start as notes for a data mining class that one of us (Nitin Patel) was teaching at MIT, and was completed while another of us (Galit Shmueli) was teaching a similar course at Maryland.Both courses were part of an MBA program.We found that, while there are a lot of books on data mining, there were none that actually gave business students the skills and tools to implement data mining algorithms.So we set ourselves the task of writing a book that (1)provides real data sets with a business decision-making context and a hands-on orientation , (2)provides a theoretical and practical understanding of the key data mining methods of classification, prediction, data reduction and exploration at a level that is appropriate and useful for MBA's, and (3) bundles a powerful version of a commercial data mining tool that works in Excel (XLMiner).For this reason, we think our book will be appropriate not just for students, but also for business analysts with a quantitative orientation, on, indeed, anyone who wants to learn data mining via self-study.Have we succeeded?You be the judge!- P. Bruce (for G. Shmueli and N. Patel) ... Read more

Book Description
Breezy black criminal attorney Tara Delacruz gets unpopular clients exonerated while her rival, the white-bread mayor and secret immortal, Cord Smith, wants them off his streets.Somewhere along their craggy coast, a fiend buries his victims at low tide at neck level.When the tide rises, they don't drown.Their town is spooked.Cord knows someone has found an evil use of immortality and believes the lawyer may have run across him.When he's forced to speak with her, he falls for the woman he censures.Bound to a secret conspiracy, he can't share everything, yet he warns her not to accept this twisted case. ... Read more

Customer Reviews (3)

minning evermore
this book was veryentertainingfast pace , would read more from this author

Fantastic
MINING EVERMORE had me shivering when I wasn't magically drawn to Tara and Cord during their perilous search for a sinister fiend.Their search ended in a surprise, but it fit.I adored Cord's conviction to keep Tara safe and loved him when he was angry and frustrated when he couldn't.MINING EVERMORE will leave you believing in love with all its major transformations.

Mining Evermore is a great read!
Kathleen Rowland put her heart and soul in the writing of this book and it shows.She keeps you right in there, wondering who is committing the crimes.She's got an interesting twist on immortality unlike anything I've seen in other paranormal books.Her development of the characters is masterful.I think you'll enjoy this and won't be sorry you picked it up. ... Read more

Book Description
An introductory text and reference on mining engineering highlighting the latest in mining technology
Introductory Mining Engineering outlines the role of the mining engineer throughout the life of a mine, including prospecting for the deposit, determining the site's value, developing the mine, extracting the mineral values, and reclaiming the land afterward. This Second Edition is written with a focus on sustainability-managing land to meet the economic and environmental needs of the present while enhancing its ability to also meet the needs of future generations. Coverage includes aboveground and underground methods of mining for a wide range of substances, including metals, nonmetals, and fuels.
Completely up to date, this book presents the latest information on such technologies as remote sensing, GPS, geophysical surveying, and mineral deposit evaluation, as well as continuous integrated mining operations and autonomous trucks. Also included is new information on landscape restoration, regional planning, wetlands protection, subsidence mitigation, and much more.
New chapters include coverage of:
* Environmental responsibilities
* Regulations
* Health and safety issues
Generously supplemented with more than 200 photographs, drawings, and tables, Introductory Mining Engineering, Second Edition is an indispensable book for mining engineering students and a comprehensive reference for professionals. ... Read more

Customer Reviews (2)

BARGAIN
I originally ordered this book over the SME website. However after doing so, I looked for the book on amazon and found it way cheaper.So I canceled the SME order and bought from Amazon.It's the same book and everything and was received via shipping in reasonable time as well. Great Bargain!

mining engineering
I purchased this book for a class. It is very informative. The way they explain things can be annoying at times. There are lots of references in the middle of paragraphs that are unecessary. There are also many times where they say "go to this book to find out about this subject". It's good if you actually want to go look for other books, but annoying if you are just reading this one. The graphs and pictures are very helpful. Overall this book explains the concepts well. ... Read more

Book Description

Mining Group Gold is a book on leadership. It explores the process of managing people and ideas to achieve a high level of results in a complex, turbulent global economy. This book is a practical, easy to use guide to building and maintaining collaboration within and across teams.

Includes:

Customer Reviews (6)

Golden Ideas for Mining Gold from Your Groups
This is really as much a book about effective leadership and management as it is about group facilitation. Solidly grounded in the author's experience at Xerox, it teaches everything needed to make your group sessions far more effective than it currently is. It also includes guidesheets, tables and surveys that you will find useful in planning, facilitating and evaluating your sessions.

Mining Group Gold
I read Tom Kayser's book in the early 1990's when it was originally written for Xerox.The new edition is a fine adaptation.The book has been immensely helpful to me as a facilitator.It gives great instructions to a new and experienced group leader.

Mr. Kayser understands group dynamics.By reading and more importantly, following his "recipes", you will find that meetings are both more efficient and effective.

Treasure Trove of Productivity
One of the most valuable lessons I learned in my brief time as a researcher at the Xerox Palo Alto Research Center was that meetings need not be unending wastes of times. When I first noticed this and asked how itwas possible that so much always seemed to get done, no mater who wasinvolved, I was given a copy of the Tom Kayser book "Mining GroupGold".

What a wonderful and unexpected surprise reading it was!Although concrete steps are given to guide people into getting the most outof working together and, more important, thinking together, its basic valuecomes from the way it enables folks to remain themselves - only do a betterjob of it.

Since leaving Xerox three years ago to be a consultant forMicrosoft, General Electric and Sun Microsystems, I've given numerouscopies to new cohorts so as to make life easier and the flow of work moreefficient for all.

Tap the Mother Lode of Collaborative Cranial Creativity
Thomas A. Kayser has set forth a systematic means for gaining the maximum benefit from meetings.

Starting with John Kenneth Gailbraith's infamous attribilious amphigory: "Meetings are vital to those who want to makesure nothing happens" Kayser leads the reader through the labyrinth oftraditional meeting quagmires and enables everyone to profit from combiningtheir brain power optimally.

In practical, no-nonsense terms, nothing isleft to chance. Those who practice the lessons will reap the immediate andcontinued rewards from the old axiom of time being money!

most important book about making teams effective available
I have used what I learned by reading Mining Group Gold in a wide variety of consulting and operational settings over the past three years: with multi-functional teams in a medium sized data communications hightechnology company and a start-up software development company, with theboard of directors of a subsidized housing agency, with teachers andadministrators at a primary school, with groups of principals responsiblefor implementing new curriculum in their schools, and with senioradministrators at a leading geriatric care facility.

I speak fromexperience, then, in stating that Mining Group Gold is straight-forward,intuitively sound technology which can have profound effect on the style offacilitators in carrying out their difficult roles, and on ALL members of ateam, a work group, (or even a board of directors!) to increase their senseof ownership, and their commitment to outcomes.

It's an easy read, and Irecommend it as the best available resource in my experience for creatingand continually improving meeting effectiveness. ... Read more

Book Description
The mining industry is vital to the Australian economy, accounting for around 320f annual exports. The Mining Valuation Handbook is the most comprehensive book published on this subject. This Premium Finance edition has been fully revised, expanded and updated.

This book provides mining information for the financial industry and financial information for the mining industry. Topics covered include:

• feasibility studies
• commodity values and forecasting
• classification of resources and reserves
• indicative capital and operating costs
• valuation and pricing techniques
• quantifying risk
• the impact of exploration and expansion.

And there is much, much more. As Robert Champion de Crespigny writes in the foreword, this book 'unravels many of the mysteries and valuation techniques employed by resource industry specialists and the share market analysts who follow the sector.' ... Read more

Customer Reviews (1)

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

Book 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.Download Description
Business managers need to be able to mine company databases to find new ways to improve customer sales and support. This book/CD-ROM dynamic set provides models of the most commonly asked data mining questions. ... 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

Book Description
This new edition, containing 28,500 terms, incorporates the technological developments and environmental regulations that have changed the minerals industry so dramatically. It is the culmination of a 5-year effort incorporating not only standard mining-related terms but also terms in peripheral areas, such as the environment, marine mining, leaching, pollution, automation, health and safety. Many of these terms now have a legal definition based on law or regulation. ... Read more

Book 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 (2)

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