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Introduction to Data Mining Hardcover – May 12, 2005

ISBN-13: 978-0321321367 ISBN-10: 0321321367 Edition: 1st
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Product Details

  • Hardcover: 769 pages
  • Publisher: Pearson; 1 edition (May 12, 2005)
  • Language: English
  • ISBN-10: 0321321367
  • ISBN-13: 978-0321321367
  • Product Dimensions: 7.7 x 1.6 x 9.3 inches
  • Shipping Weight: 3.1 pounds (View shipping rates and policies)
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (30 customer reviews)
  • Amazon Best Sellers Rank: #165,330 in Books (See Top 100 in Books)

Customer Reviews

3.9 out of 5 stars

Most Helpful Customer Reviews

71 of 72 people found the following review helpful By Sandro Saitta on September 20, 2006
Format: Hardcover
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.
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54 of 57 people found the following review helpful By W Boudville HALL OF FAMEVINE VOICE on July 31, 2005
Format: Hardcover
As databases keep growing unabatedly, so too has the need for smart data mining. For a competitive edge in business, it helps to be able to analyse your data in unique ways. This text gives you a thorough education in state of the art data mining. Appropriate for both a student and a professional in the field.

The extensive problem sets are well suited for the student. These often expand on concepts in the narrative, and are worth tackling.

The central theme in the book is how to classify data, or find associations or clusters within it.

Cluster analysis gets two chapters that are superbly done. These summarise decades of research into methods of grouping data into clusters. Usually hard to do, because an element of subjectivity can creep into the results. If your data is scattered in some n-dimensional space, then clusters might exist. But how to find them? The chapters show that the number of clusters and the constituents of these can depend on which method you adopt, and various initial conditions, like [essentially] seed values for clusters, if you choose a prototype cluster method like K-means.

The descriptions of the cluster algorithms are succinct. Why is this useful? Because it helps you easily understand the operations of the algorithms, without drowning you in low level detail. Plus, by presenting a meta-level comparison between the algorithms, you can develop insight into rolling your own methods, specific to your data.

Part of my research involves finding new ways to make clusters, and the text was very useful in explaining the existing ideas.
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38 of 42 people found the following review helpful By Dr. Lee D. Carlson HALL OF FAMEVINE VOICE on April 17, 2006
Format: Hardcover Verified Purchase
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 of the main problems in data mining is to find suitable `interestingness measures'.
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23 of 26 people found the following review helpful By Dylan on April 7, 2009
Format: Hardcover
Clear, easy to read text that covers many topics of data classification and mining (as described by the several other reviewers).

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

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

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