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
"Berry and Linoff lead the reader down an enlightened path of best practices." -Dr. Jim Goodnight, President and Cofounder, SAS Institute Inc.

"This is a great book, and it will be in my stack of four or five essential resources for my professional work." -Ralph Kimball, Author of The Data Warehouse Lifecycle Toolkit

Mastering Data Mining

In this follow-up to their successful first book, Data Mining Techniques, Michael J. A. Berry and Gordon S. Linoff offer a case study-based guide to best practices in commercial data mining. Their first book acquainted you with the new generation of data mining tools and techniques and showed you how to use them to make better business decisions. Mastering Data Mining shifts the focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management.

In this book, you'll learn how to apply data mining techniques to solve practical business problems. After providing the fundamental principles of data mining and customer relationship management, Berry and Linoff share the lessons they have learned through a series of warts-and-all case studies drawn from their experience in a variety of industries, including e-commerce, banking, cataloging, retailing, and telecommunications.

Through the cases, you will learn how to formulate the business problem, analyze the data, evaluate the results, and utilize this information for similar business problems in different industries. ... Read more

Customer Reviews (7)

A master piece
Although aged the book remains a precious guide for business and CRM people. It argues that data mining is a discipline that must be mastered by concentrating in how to approach analytics than how to use tools. Specifically it stresses the importance of:
- the iterative nature of data mining activities (and project life cycle)
- the active involvement of business people
- the business objectives and needs
- the preparation and split of the model set (mining view)
- the evaluation of produced models and patterns
- the business interpretation of data mining results
- the power of data exploration (by example)
"Mastering Data Mining" is a much more concrete and comprehensive book than "Data Mining Techniques, 2nd edition".

Great superficial knowledge but falls short overall
Mastering Data Mining is a great book for quick superficial reference or a crash course in data mining but it becomes useless as more complicated issues araise. The book has a lot of practical examples and quick tips on the outside but as soon as you begin scratching the surface you find out that the examples are as general as they are vague. Some important points in model building are completely omitted and hidden with a graph or nice looking footnote.

More than once I finished a chapter wondering how some model or technique was used. I would suggest reading only the first eight chapters which are a great introduction to overall data mining and skip the case studies. If you are expecting a more serious and detailed reading on data mining, look somewhere else because you won't find it here.

Ideas for GUI design of data mining software
While doing a graduate elective on Decision Making Technologies, I realized that data visualization and representation is crucial for data exploratory and validation of data mining analysis. To get some ideas on how the various data visualization and workflow techniques are applied and integrated into the GUI of commercial softwares, survey the various chapters of this book.

Excellent book!
This book is an excellent book.The authors explain the various techniques, and show real world examples of their use.Most importantly, they explain the underlying goals of the various techniques, and what towatch out for when using them.I was most relieved to read that I am notalone in having limited success with association rules!

Although someof the particular examples were not the type of examples I deal with, thereasons they were chosen make perfect sense.Data mining owes much of itspopularity to people attempting to find churners, etc.But there areplenty of examples covered, and with each one some new insight is revealed. Especially useful to me were the explanations of what it is one sees inthe decision trees, lift curves, etc.Also, seeing various problems solvedwith several of the popular tools (MineSet, Enterprise Miner, etc.) wasvery helpful. There are many examples from various industries, and youlearn something new about those industries too!(If you like the SesameStreet videos of how cans, tires, etc. are made even more than your kidsdo, you'll love this book for the examples alone.)

It is clear from thisbook that the authors not only know what they are talking about, they canactually break it down for a newbie like me.I have also had the pleasureof being in one of Mr. Berry's MineSet classes, and he demonstrated thesame depth of knowledge and ability to convey it to others in that class aswell.

This book is not an algorithm book, but it touches on them.It isnot necessarily a tour of data mining tools, but does do this to somedegree.It is probably most useful for anyone who wants to know "Whatis this 'data mining', and how can it help me?" with real worldexamples to make things clear.If the reader starts out thinking that datamining is just tossing a bunch of data into a tool and getting concreteresults back, the confusion will not remain after reading this book. Finally, this book is VERY easy reading.Do yourself (or your boss) afavor and buy this book!

Good, not Great
This book provides a number of case studies on applying data mining. I didn't learn a lot since the studies weren't applicable to what I am doing. Someone else might get more out of than me though.I did like theirfirst book (it was very good) but this one wasn't nearly as good. Thereare better books that discuss the use of data mining software. ... Read more

Product Description

Product Description

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing.

Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle shares his own successful data preparation methodology, offering both a conceptual overview for managers and complete technical details for IT professionals. Apply his techniques and watch your mining efforts pay off-in the form of improved performance, reduced distortion, and more valuable results.

On the enclosed CD-ROM, you'll find a suite of programs as C source code and compiled into a command-line-driven toolkit. This code illustrates how the author's techniques can be applied to arrive at an automated preparation solution that works for you. Also included are demonstration versions of three commercial products that help with data preparation, along with sample data with which you can practice and experiment.

* Offers in-depth coverage of an essential but largely ignored subject.
* Goes far beyond theory, leading you-step by step-through the author's own data preparation techniques.
* Provides practical illustrations of the author's methodology using realistic sample data sets.
* Includes algorithms you can apply directly to your own project, along with instructions for understanding when automation is possible and when greater intervention is required.
* Explains how to identify and correct data problems that may be present in your application.
* Prepares miners, helping them head into preparation with a better understanding of data sets and their limitations. ... Read more

Customer Reviews (13)

Wrong Audience
I have a great deal of experience preparing data for analysis.I was looking for a book that would add to my understanding of and enhance my organization for data preparation.This is not that book.At best, the book provides insight into the types of issues faced in preparing data and emphasizes the value of such.Rather than criticize, I wish to foreworn those who have already practiced at a somewhat rigorous level (more than five semesters of statistics/data mining) that this might not be what you are seeking.

Great Book
As the old saying goes, Garbage In, Garbage Out (GIGO)."Data Preparation for Data Mining" is the remedy for this pervasive, age-old problem.There are so many different aspects to data quality, it boggles the mind.Mr. Pyle addresses each one in detail, with clear examples and explanations.

The book is well-written and more importantly, understandable.It should be required reading for every researcher and modeler BEFORE they begin their careers.The way data is prepared and aggregated determines the picture one gets from the data.It must be done correctly from the start, or all downstream processing and conclusions are suspect.

The CD that comes with the book is pretty much useless, but aside from that caveat, this is a great book.Buy it - you won't be disappointed.

Still very much worth reading in 2007
I have been helping folks learn Clementine - a data mining package - for several years. I have read a number of related books, but never got to this one until recently. That was a mistake. This may be an important book for you if you are new to Data Mining, even if, especially if, you already have expertise in statistics and/or data base technology.

Although I still believe if someone is brand new to the field that they begin with Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, this should be the second book that they read. Far too many books in this area read like statistics books (notably Data Mining Methods and Models).

Statistics training can be of enormous benefit to data miners, but leads to certain predictable errors. Not only that, many data miners already have statistics training and that just compounds the likelihood that they will make these mistakes when the book author fails to show the difference clearly. Pyle performs consistently well in this regard. He consistently focuses on the kinds of problems data miners are likely to see in their work.

To give just a couple of examples: Few variables will be already stored as continuous, normally distributed variables; principle components analysis might sometimes be a problematic way to eliminate predictors and even be dangerous; missing versus "empty" data; constantly present non-linearity.

His practice data set has a real variety of variable types, and dozens of predictors. If you are figuring out if Data Mining can help you, start with the Berry/Linoff book. But .. if you are about to begin in earnest read this book. Then, time permitting; start reading specific books on modeling or software. For instance, another Larose book has good, detailed coverage of algorithms, and some information on Clementine. Discovering Knowledge in Data: An Introduction to Data Mining

The best book on this subject
There are more books available now on data mining preparation but Pyle's book is the best one :
- very clear
- covering all the important topics to need to learn about
- based on many examples and advices coming from real life
To buy absolutely

Excellent book
I started to work in Data Mining about 7 years ago. I have read many books about this subject and nearly all of them have similar approaches and stress the importance of the data preparation but they all, except one, just discuss lightly this subject. I don't know any book that deal with data preparation as Pyle's book does. Each topic is discussed in-depth. This is a very clear and enjoyable book. I have tested all concepts and the results are amazing.

Chapter 11 introduces the Data Survey topic in which techniques based in Shannon's Information Theory are showed. These information theory approaches are simply wonderful and gives to the Data Mining subject the bases to get models that extract complete relevant information from the data.

I recommend this book to anyone who wants to work seriously with Data Mining. ... Read more

Product Description
This book from the America at Work series introduces young children to the people, machines and environmental concerns involved in the resource-based industry of mining. In Mining, kids explore an underground mine, a surface coal mine and an oil drilling site. Combining fact and fiction with colorful illustrations, the story delivers an early lesson in appreciating and protecting our natural resources. ... Read more

Product Description
Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability.

Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity.

This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. ... Read more

Customer Reviews (4)

Great Need to Know Info on Ensembles
This is a really great (short) book in my opinion. It contains the best "need to know" information found in the Elements in Statistical Learning, and other good books on data mining. The included R code is a big bonus. I am enjoying reading it so far, and I highly recommend it. The only thing that frustrates me is that the online version on the publishers website is in color, while the print version is not. This is the only reason I did not give it 5 stars. I saw the online version first, and thought that the print version would be in color as well. I am sadly mistaken. There are many graphics in this book that reference different colors and it just looks really crappy in grayscale. If you are familiar with the Elements of Statistical Learnining, imagine printing that out in grayscale and you will know what I mean.

really helpful in learning the method
During my 10 plus years of modeling experience, I have always paid most of my attention on variable selection, predictive power, effectiveness and efficiency of a single model form such as logistic model, ordinary regression, tree, etc. From time to time, I also segment my sample space into pieces and then apply different modeling techniques. Never really aware of the concept of 'model selection' or 'model combination'. That classical approach has served me well. But I always suspected that there was a better approach to combine different methods to get better predictions.
Ensemble methods detailed in this books gave me the 'ah ha'. It gave a nicely balanced flavor of easy implementation and difficult concepts. I really enjoyed the book. I was able to finish the book quick and would save it for reference.
If there is anything that I would want to see in more detail, it is the treatment of evaluation of model prediction. It is a bit light on how to tell if the final product is really working. Given that the book is an intro, then it is not really a mis-treatment.
overall, awesome small book.

The definitive reference on ever-important ensemble models
The definitive reference on ensembles, which are a central, profound aspect of predictive modeling best practices. A solid, core, important reference with great coverage and tutorial value.

A much needed guide to ensemble methods
"Ensemble Methods in Data Mining" (EMIDM) is an excellent introduction and reference to ensemble methods, from several perspectives:

- good mathematical descriptions of the algorithms;
- intuitive explanations of the concepts involved;
- several illustrative examples (including R code); and
- a great structured guide to the vast literature on ensemble methods

The only other book I know that covers ensemble methods is the well-known "The Elements of Statistical Learning" (TEOSL), which can be quite a dense read at times.For example, TEOSL covers Importance Sampling Learning Ensembles (ISLE) and Rule Ensembles (RE) in a couple of pages each, whereas EMIDM dedicates a chapter to each (personally, I had overlooked the significance of those 2 methods until I read the more developed narrative in EMIDM).

In any event, you should own a copy of TEOSL (which can be freely downloaded off the authors website), but if you want to master ensemble methods (currently one of the hottest areas in data mining and machine learning) and confidently be able to apply them in practice, then EMIDM is a wise investment. ... Read more

Product Description
A complete and comprehensive handbook for the application of data mining techniques in marketing and customer relationship management. It combines a technical and a business perspective, bridging the gap between data mining and its use in marketing.
 
It guides readers through all the phases of the data mining process, presenting a solid data mining methodology, data mining best practices and recommendations for the use of the data mining results for effective marketing. It answers the crucial question of 'what data to use' by proposing mining data marts and full lists of KPIs for all major industries.Data mining algorithms are presented in a simple and comprehensive way for the business users along with real-world application examples from all major industries.

The book is mainly addressed to marketers, business analysts and data mining practitioners who are looking for a how-to guide on data mining. It presents the authors' knowledge and experience from the "data mining trenches", revealing the secrets for data mining success. ... Read more

Customer Reviews (10)

Higly recommended
Challenging, yet highly insightful. I was impressed with the breadth of subject matter, and found many opportunities for learning.

A must - read handbook for Data Mining
This is a powerful hand book for all marketers.
Illlustrated with real world applications concerning banking and telecommunications, it provides a detailed and clear step by step description of data mining procedures.

Excellent business book
I would like to congratulate the authors for their excellent book. It's an original business book which really helps analysts and managers with day to day business matters.
It offers a realistic and detailed presentation of what is the correct way for a CRM department to focus on.
I would absolutely recommend it in view of the fact that it offers the necessary knowledge on Data Mining techniques in a detailed and easy to read fashion.


Kyriakos Kokkalas
CRM manager
WIND (WIND Telecommunications S.A.)

A pragmatic, hands-on approach
A holistic customer behavior & attitude analysis to support decision makers. It is the 'Bible' for marketers who need to build a deeper insight and plan accordingly their actions per customer segment, building profitable customer relationships. I truly recommend using it in your business.

Efi Kassouri
IT/ Release Management Senior Professional
Vodafone

A practical book for the professional and an informative guide for the scientist
This book is an excellent guide for the practitioner, a data mining specialist who needs to do something with his/her customer information. It covers basic machine learning algorithms like kmeans, decision trees and self organization maps, pca, etc. I recommend this book as a starting pointfor people who don't have any experience with data mining and statistics and they want to do something with their data. It is a big plus for the book that it gives hints about how to choose the options for algorithms, since this is very critical and hard to find. We as a company use this book to understand the problems that the business world faces. I think the book has the right size and sufficient number of examples that are very well explained.
Although I understand the need of picking a tool to express the examples I think sticking to SPSS so tightly gives a bias to the book of what is feasible and what is not. I think some examples with open source like R would help the book to be less biased. But again I don't think it is a major issue, since the majority of the audience is professionals that will buy SPSS.

My major objection though, has to do with the distinction of machine learning and statistical learning that authors make. As it is stated in page 61 decision trees and neural networks are machine learning methods and notstatistical ones, while pca is a statistical one and not a machine learning one. In reality all of them are statistical methods, machine learning and statistical learning are the same thing in the literature. A more sensible taxonomy between statistical methods is parametric/non-parametric. It is true that in general parametric methods can be faster versus non-parametric, but the statement in the book that they are more accurate is not in general valid.Nonparametric methods are slower but they are by far the most accurate. In reality though, nonparametric methods have beenaccelerated recently and they can actually be as fast as parametric ones.

I definitely recommend this book and I think the authors have done very good job, it filled a gap between science and business.

Nikolaos Vasiloglou
CTO Analytics 1305

... Read more

Product Description

Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.

Product Description
This SME classic is both a reference book for the working engineer and a textbook for the mining student. This hardcover edition gives a brief history of surface mining and a general overview of the state of surface mining today--topics range from production and productivity to technological developments and trends in equipment.

This extremely useful text takes the approach that exploration and mining geologists must be expert in a number of fields, including basic finance and economics, logistics, and pragmatic prospecting. Readers will find material on all these topics and more.

The book's nine chapters include:Introduction,Exploration and Geology Techniques,Ore Reserve Estimation,Feasibility Studies and Project Financing,Planning and Design of Surface Mines,Mine Operations,Mine Capital and Operating Costs,Management and Organization, andCase Studies.

The book is fully indexed. ... 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

Customer Reviews (1)

a very good pic. of the development of Butte as an Irishtown
David M. Emmons, in The Butte Irish, examines the development of Butte, Montana, as an Irish town, tracing the story from the Potato Famine to about 1925.He focuses on two major questions: (a) What made Butte such apopular destination for Irish immigrants, both directly from Ireland andfrom other Irish areas of the US? and, (b) How did the development of anIrish enclave in Butte affect the development of the city? He goes on toexamine the evolution of class relations within the Irish in Butte. Emmons describes Butte as a unique location in America for the study of anethnic community.He argues that the town developed in such a way and atsuch a time that it was one of the only towns in the country to have astrong working-class, immigrant community in a position of major influenceand power.There were several keys that made this path of cityevolution possible.The first was the switch from silver and gold miningto copper production in the 1870's.This is key for Butte's"Irishness" on several levels.First, because of the largecapital investment required for copper mining, Butte was forced toindustrialize to a much greater extent than other major gold and silvermining camps of the West.Thus, Butte was the only one of these miningcamps to become a major city.Immigrants from many of these camps came toButte in large numbers.The timing of the beginning of Butte'scopper era is a second major factor.The Irish Potato Famine of the 1840'scaused huge numbers of Irish to immigrate to America.In the yearsimmediately following the famine, the Irish were nearly forty percent ofthose immigrating to the United States.Large numbers of Irish continuedto immigrate in the next thirty years, supplying the US with many unskilledworkers.Many of these Irish went to the mining camps of the west, thecoal mines of Pennsylvania, or the copper mines of Michigan, because miningwas one of the only industries they were familiar with.As many of thewestern mining camps became "played out," or ran out of viableore, in the late nineteenth century, the Irish looked to the developingButte.Because Butte was becoming an established city only when theIrish started going there, it did not have a previously existing communityof entrenched middle class Americans, nor did it have a prior politicalstructure.This is another key difference between Butte and other townswith sizable Irish populations such as Boston or San Francisco.Inpre-existing towns and cities, the middle class often looked down on thoseof the working class, or at least had control of the political and socialstructure of the area. It is a well-known fact that Marcus Daly was oneof the main reasons so many Irish came to Butte.Daly was the owner of theAnaconda Mining Company, and a strong Irish nationalist.His hiringpolicies were famous throughout the West, and even in Ireland, as beingvery generous to the Irish.Emmons lays out these reasons, detailingthem extensively.His research was thorough, utilizing "two fullcarloads" of primary materials including records of Butte churches andIrish social organizations, letters, newspapers. Also cited in Emmons'bibliography are extensive interviews and secondary sources.Emmonsis just as thorough in his treatment of the second question.He considersthe miners of Butte on many levels.One of the more interesting themes ofthe book is the discussion of conflicting loyalties within the Irishenclave of the Mining City.The author frames this as the question ofwhether the people considered themselves "workingIrish-Americans" or "Irish-American workers."He examinesthe politics of the struggling Ireland and its relationship with England,the structure of the Butte social organizations and the way their roles andimportances, both absolute and relative to one another, changed and grewduring this period, and changing demographics within the Irish and the restof Butte-Silver Bow.The only complaint to be lodged against TheButte Irish is the author's occasional use of difficult sentence structure. I can't find the quote I was going to use here, but there were a few tochoose from.The Butte Irish is a well-written and well-executedaccount of the development of a town and community, offering many insightsinto working class ethnography, labor relations, Montana history, and Irishhistory, among others.Emmons has managed to cover aspects of all theseareas, even while maintaining a strong focus and cohesiveness throughoutthe book. ... Read more

Product Description
Primer on data mining provides an introduction to the principles and techniques for extracting information, from a business-minded executive. Data sets from the CD-ROM are used in examples and exercises. Softcover. ... Read more

Customer Reviews (7)

Good Intro, Best with other material
This book is touted as an intro to Data Mining, and while it does cover most of the intro topics you'd want to learn about, it seems to cover some in more depth. This was a text book for an Information Organization/Retrieval course I took, but our instructor subsidized it pretty heavily with other sources and her own notes. Worked very well when used in a classroom setting.

Text Fair, iData Analyzer CD Software Problematic
The iData Analyzer Software from infoacumen.com on CD included with the text is problematic at best.Students in my Business Intelligence and Data Warehousing courses have had many problems with installation and with running Data Mining Sessions even with the included sample worksheets.Downloading the 'latest' version of the software directly from the infoacumen web site did not improve the situation.Inquiries to the infoacumen site support email address have gone without reply. Software runs in 'compatibility mode' in Excel 2007 and even with macro security turned off, Excel often crashes.Data Mining Sessions often fail with a variety of error messages even with running the supplied sample worksheets.The Excel add-in interface is completely devoid of any GUI and no graphic output of Data Mining Sessions are available.To work around these problems I have had to ferret out sample worksheeets that would not crash Excel and post the Data Mining Session results for review and interpretation.As an alternative we have made extensive use of Microsoft SQL Server 2008 Analysis Services Data Mining Project Options and Tools and Oracle Data Miner software.

The text offers a fairly good treatment of Data Mining, however many topics are presented in a manner and discussion over complicating various subjects.

A very good choice for learning data mining concepts with minimal resources
The particularity of this book is that it is more accessible to read than most of data mining books, which in general require some maths/statistics/computing background.

The book is not written in the best way from the point of view of a data mining expert, as for instance sometimes a theme is recurrent in the text, but it is not obvious to explain data mining concepts using minimal previous knowledge in computing/maths/statistics.

A second important positive aspect is that the book comes with a software (IDA) running under Excel, which can be used to illustrate the techniques presented in the book (BTW a new version of the software is freely available to download, regularly). This is not the case with most of the data mining books. So if you wish to learn the basics of data mining with minimal or no previous resources (good maths/computing background and access to expensive data mining software) then this is a very good choice.

A poor book, even for beginners in DM
The author is not able to clearly present/describe concepts without using many examples.For many subjects(even simple ones), the book is lacking in clarity and logic, so that it's hard to find out what the author is trying to say.Too many pages are wasted on irrelevant sentences. Don Box books contain difficult logic, but you feel his clarity and preciseness in his writing.George Shepherd books are also enjoyable to read even though the subjects covered are wide and not easy. This book is just the opposite: easy concepts presented with a messy, hard-to-follow style.

Inconsistent Depth
When I bought this book, I knew nothing about data mining. Unfortunately, this book glossed over the topics I knew least about and spent a depressing amount of time on stuff anyone should have learned by junior high. They introduced 188 "Key Terms" in a book that's only 350 pages long. In chapter 1 they give definitions for words like "fact", "hypothesis", etc. Yet by chapter 5 they start flinging the symbols for attribute standard deviation at you with no explanation or warning. So I'm not sure who they think will be reading their book...but from what I can tell, they assume their target audience can handle advanced algebra with ease but may need a definition of "the scientific method".

They also spend quite a bit of time walking you through the Excel PivotTable creation wizard and other such fluff. They carefully instruct the reader that dragging and dropping is accomplished through use of the mouse, and that you should drop columns into the area marked 'Drop Column Fields Here'.

On the upside, I do know a bit more about data mining now. I don't feel that I could run right out and get a job, but at least when I start reading another book I'll have an idea of what the terms and concepts are.

So I suppose if you're good at statistics, have never taken a basic science course and have poor computer skills, this book is for you. ... Read more

Product Description
Subjects: Mines and mineral resourcesNotes: This is an OCR reprint. There may be numerous typos or missing text. There are no illustrations or indexes.When you buy the General Books edition of this book you get free trial access to Million-Books.com where you can select from more than a million books for free. You can also preview the book there. ... Read more

Product Description
The Navajo Nation covers a vast stretch of northeastern Arizona and parts of New Mexico and Utah. The area is also home to more than one thousand abandoned uranium mines and four former uranium mills, a legacy of the U.S. nuclear program.

In the early 1940s the Navajo Nation was in the early stages of economic development, recovering from the devastating stock reduction period of 1930. Navajo men sought work away from the reservation on railroads and farm work in Phoenix and California. Then came the nuclear age and uranium was discovered on the reservation. Work became available and young Navajo men grabbed the jobs in the uranium mines.

The federal government and the mining companies knew of the hazards of uranium mining; however, the miners were never informed. They had to find out about the danger on their own. When they went to western doctors, they were diagnosed with lung cancer and were simply told they were dying.

A team of Navajo people and supportive whites began the Navajo Uranium Miner Oral History and Photography Project from which this book arose. That project team, based at Tufts University School of Medicine in Boston, recruited the speakers who told their stories, which are reproduced here. There are also narrative chapters that assess the experiences of the Navajo people from diverse perspectives (history, psychology, culture, advocacy, and policy). While the points of view taken are similar, there is a range of perspectives as to what would constitute justice.

REMEMBRANCE TO AVOID AN UNWANTED FATE

by Navajo Nation President Joe Shirley, Jr.

Sixty years ago, the United States turned to the tiny atom to unleash the most destructive force known to mankind and bring an end to World War II. Ironically, the uranium used to create the most technologically advanced weapon ever invented came from the land of the most traditional indigenous people of North America, and was dug from the earth with picks and shovels.

Nuclear weapons transformed the United States into the greatest military force the world has ever known, and the term "Super Power" was coined. Lost in the history of this era is the story of the people -- the Diné -- who pulled uranium out of the ground by hand, who spoke and continue to speak an ancient tongue, and who pray with sacred corn pollen at dawn for good things for their families. By the thousands, these were, and remain, the forgotten victims of America's Cold War that uranium spawned.

The Navajo People and Uranium Mining is the documented history of how these Navajo people lived, how they worked and now, sadly, how they died waiting for compassionate federal compensation for laboring in the most hazardous conditions imaginable, and which were known at the time yet concealed from them. These Navajo miners and their families became, in essence, expendable people.

Today, the Navajo Nation, with the help of law firms, environmental groups, writers, photographers and historians, is doing all it can to correct this horrendous wrong done to Navajo uranium miners, their families and their descendents. This excellent book allows the people who lived this to tell their story in their own words.

Genocide. There is no other word for what happened to Navajo uranium miners. The era of uranium mining on Navajoland was genocidal because the hazards of cancer and respiratory disease were known to doctors and federal officials, and yet they allowed Navajos to be exposed to deadly radiation to see what would happen to them. As a result, radiation exposure has cost the Navajo Nation the accumulated wisdom, knowledge, stories, songs and ceremonies -- to say nothing of the lives -- of hundreds of our people. Now, aged Navajo uranium miners and their families continue to fight the Cold War in their doctors' offices as they try to understand how the invisible killer of radiation exposure left them with many forms of cancer and other illnesses decades after leaving the uranium mines. No one ever told them that mining uranium would steal their health and cripple their lives when they became grandparents. But it did. They continue to leave us to this day only because they were the ones who answered the call.

Because of this painful history, in 2005 the Navajo Nation passed the Diné Natural Resources Protection Act. This law prohibits uranium mining and processing in all its forms on Navajoland. It protects our land and our water from being contaminated as it was in the past. Despite our sovereignty and our will, there are those today who still seek to weaken our resolve in order to gain access to the uranium under our land just to enrich themselves. Only the telling of this story, as The Navajo People and Uranium Mining does so excellently, can protect us from this unwanted fate and a repeat of one of the more sorrowful periods of the Navajo Nation's history. ... Read more

Customer Reviews (1)

Critical Aclaim

"...a poignant, bittersweet oral history...The University of New Mexico Press is to be commended for making [The Navajo People and Uranium Mining]...--The Times-Independent, Moab, UT

"...an excellent book that helps to fill the information void regarding the Navajo people and their uranium mining experience. This book provides for the Navajo people access to a large audience of readers to whom they can tell their personal and unheard stories."--Environmental Health Perspectives

"...an important study [that] provides a clear, wide-ranging analysis of the impacts that uranium mining and milling left in Navajo Country over the past sixty years. The editors skillfully weave together diverse issues surrounding the entangled history of uranium and the Navajo Nation."--Utah Historical Quarterly

"...each chapter fills a gap in the complex story of the relationship between the Dine and the yellow ore."--Navajo Times

"...moving conversations balance the carefully researched analytical chapters and give the book its emotional depth and originality."--Indian Country Today

"...not a typical historical narrative from one author's perspective...[instead] a unique account of the tragic Navajo experience told by those who have worked in the mines and live through the physical and mental health consequences....a fascinating look into the personal history of the Navajo people in the U.S. nuclear program."--Public Health Reports

"...these interviews present illuminating and often heartbreaking stories...an excellent contribution for its engagement in both local Navajo knowledge and activist struggles for compensation and for its broader effort to educate the non-Navajo public about issues of indigenous environmental justice."--Western Historical Quarterly

"...this book is of great relevance and necessity in the documentation of human trauma and the survival of the Navajo people."--New Mexico Historical Review

"The Navajo People and Uranium Mining contributes to the growing scholarship on the Atomic West and should be read by those interested in modern Native American history and health, atomic and mining history, and oral history."--The Journal of Arizona History

"The editors of this book present a remarkable story of determination, resistance, and perseverance in the face of intolerance, indifference, and wealthy and powerful institutions."--New Solutions

"This book is an excellent source of information for individuals, teachers, and students wanting to learn more about the story and culture of the Navajo people....Its varied perspectives could be applied to almost any part of the world struggling with the affects of uranium mining in their community."--Voices from the Earth

"This collaboration of Navajos and non-Navajo scientific specialists serves a political as well as an archival purpose....Highly recommended."--CHOICE: Current Reviews for Academic Libraries

"This important book furnishes vital insights about the short term and long term impact of uranium mining on the Navajos and on the land. The editors merit our appreciation for keeping Navajo voices at the center of this poignant and powerful volume. This is a major contribution to both Navajo history and Southwestern environmental studies." --Peter Iverson, award-winning author of Dine: A History of the Navajos

"[a] significant book"--Tribal College Journal ... Read more

Product Description
In The Trail of Gold and Silver, historian Duane A. Smith details Colorado's mining saga--a story that stretches from the beginning of the gold and silver mining rush in the mid-nineteenth century into the twenty-first century. Gold and silver mining laid the foundation for Colorado's economy, and 1859 marked the beginning of a fever for these precious metals. Mining changed the state and its people forever, affecting settlement, territorial status, statehood, publicity, development, investment, economy, jobs both in and outside the industry, transportation, tourism, advances in mining and smelting technology, and urbanization. Moreover, the first generation of Colorado mining brought a fascinating collection of people and a new era to the region.

Written in a lively manner by one of Colorado's preeminent historians, this book honors the 2009 sesquicentennial of Colorado's gold rush. Smith's narrative will appeal to anybody with an interest in the state's fascinating mining history over the past 150 years. ... Read more

Customer Reviews (1)

Colorado mining history at it's finest
WOW!This is a subject that is a passion of mine and I could not put this down. I will be ordering more of Duane's books soon. Thank's for the great read. ... Read more

Product Description

In 2004, the Government Accountability Office provided a report detailing approximately 200 government-based data-mining projects. While there is comfort in knowing that there are many effective systems, that comfort isn’t worth much unless we can determine that these systems are being effectively and responsibly employed.

Written by one of the most respected consultants in the area of data mining and security, Data Mining for Intelligence, Fraud & Criminal Detection: Advanced Analytics & Information Sharing Technologies reviews the tangible results produced by these systems and evaluates their effectiveness. While CSI-type shows may depict information sharing and analysis that are accomplished with the push of a button, this sort of proficiency is more fiction than reality. Going beyond a discussion of the various technologies, the author outlines the issues of information sharing and the effective interpretation of results, which are critical to any integrated homeland security effort.

Organized into three main sections, the book fully examines and outlines the future of this field with an insider’s perspective and a visionary’s insight.

• Section 1 provides a fundamental understanding of the types of data that can be used in current systems. It covers approaches to analyzing data and clearly delineates how to connect the dots among different data elements
• Section 2 provides real-world examples derived from actual operational systems to show how data is used, manipulated, and interpreted in domains involving human smuggling, money laundering, narcotics trafficking, and corporate fraud
• Section 3 provides an overview of the many information-sharing systems, organizations, and task forces as well as data interchange formats. It also discusses optimal information-sharing and analytical architectures

Currently, there is very little published literature that truly defines real-world systems. Although politics and other factors all play into how much one agency is willing to support the sharing of its resources, many now embrace the wisdom of that path. This book will provide those individuals with an understanding of what approaches are currently available and how they can be most effectively employed.

Customer Reviews (1)

Think of It as a 2-1/2 LB Stimulus Package
Ever encounter a concept so revolutionary that you wonder why everyone isn't talking about it? Data Mining for Intelligence, Fraud, & Criminal Detection (CRC Press 2009), by Christopher Westphal, is like that.

Admittedly, only the few, the proud, and the discerning might dare to look beyond a title and trappings ostensibly geared for hardcore law-enforcement types and techies who live and die by visual analytics and pattern recognition for crime detection and fraud prevention. Yet "Data Mining" is not only eminently readable, it contains a game-changing message practically akin to the Rosetta Stone, on how to extract sense and sensibility from all the petabytes of information and data piling up in "cylinders of excellence" (one insider's waggish term for stovepiped data) around us.

Seriously, if the content in Data Mining's pages (think of it as a 2-1/2 pound stimulus package) could be funneled to, say, Obama, Oprah, and a certain researcher at the Library of Congress, we could look forward to pole-vaulting our way through progress to peaceful prosperity - raising our quality of life while saving (as opposed to spending) billions in the process. Let's put it this way: if you like anything at all about Numb3rs, or even NCIS (the most-viewed series in America - hey, at least we've made a few steps up from Baywatch), CSI, or shows of that ilk -you owe yourself a crack at Westphal's Data Mining book. Plus, it'll give your biceps a workout, and impress the heck out of onlookers. Not to mention which, all proceeds go to the National Law Enforcement Officers Memorial Fund, which speaks well for the author'smotives on more than one front, not to mention his credibility.


... Read more

Product Description

Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program codes or using the point-and-click approach. Each chapter emphasizes step-by-step instructions for using SAS macros and interpreting the results. Compiled data mining SAS macro files are available for download on the author’s website. By following the step-by-step instructions and downloading the SAS macros, analysts can perform complete data mining analysis fast and effectively.

New to the Second Edition—General Features

• Access to SAS macros directly from desktop
• Compatible with SAS version 9, SAS Enterprise Guide, and SAS Learning Edition
• Reorganization of all help files to an appendix
• Ability to create publication quality graphics
• Macro-call error check

New Features in These SAS-Specific Macro Applications

• Converting PC data files to SAS data (EXLSAS2 macro)
• Randomly splitting data (RANSPLIT2)
• Frequency analysis (FREQ2)
• Univariate analysis (UNIVAR2)
• PCA and factor analysis (FACTOR2)
• Multiple linear regressions (REGDIAG2)
• Logistic regression (LOGIST2)
• CHAID analysis (CHAID2)

Requiring no experience with SAS programming, this resource supplies instructions and tools for quickly performing exploratory statistical methods, regression analysis, logistic regression multivariate methods, and classification analysis. It presents an accessible, SAS macro-oriented approach while offering comprehensive data mining solutions.

Product Description
Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.  The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining.

This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts.  As suggested in the preface, text mining is needed when “words are not enough.”

This book:

• Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis.
• Presents a survey of text visualization techniques and looks at the multilingual text classification problem.
• Discusses the issue of cybercrime associated with chatrooms.
• Features advances in visual analytics and machine learning along with illustrative examples.
• Is accompanied by a supporting website featuring datasets.

Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful. ... Read more

Product Description

Data Mining: Practical Machine Learning Tools and Techniques 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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.