This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.
Principles of Data Mining
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From the Back Cover
Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples & explanations of the algorithms given. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader or academic researcher to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most Helpful Customer Reviews
3 of 3 people found the following review helpful
Format:Paperback|Amazon Verified Purchase
This is really a great book for who is approaching data mining for the first time. Topics are very well presented and explained through easy example. Algorithm are summarized in clear way. After a complete overview of data mining which is covered in the first 15 pages, the author covers supervised and unsupervised technique. Really good the work done around classification and decision threes. This book could be used for teaching data mining at undergraduate and postgraduate level. It's a great place to start you journey in data mining. I deeply suggest if you are approaching this subject.
It does not cover latest trends in data mining but no book on sale actually does it. Some people might find it bit too easy or not too deep but I guess this was the author's goals.
It does not cover latest trends in data mining but no book on sale actually does it. Some people might find it bit too easy or not too deep but I guess this was the author's goals.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
4.8 out of 5 stars
4 reviews
4 of 4 people found the following review helpful
5.0 out of 5 stars
Excellent coverage & depth
5 Jan 2011
By Steven Koh
- Published on Amazon.com
Format:Paperback|Amazon Verified Purchase
I bought this book for self-study and I am very surprised by the clarity and 'just-nice' amount of depth and coverage on the topic.
I pretty much able to understand most of the content by reading the book and wiki on the more difficult topic.
I pretty much able to understand most of the content by reading the book and wiki on the more difficult topic.
5 of 6 people found the following review helpful
5.0 out of 5 stars
excellent introduction
29 May 2008
By Steve
- Published on Amazon.com
Format:Paperback|Amazon Verified Purchase
This book is an excellent introduction to data mining, concentrating primarily on decision tree induction. The material provided is presented clearly with no assumption of prior knowledge on the part of the reader. A weakness of the book is that it doesn't place the material provided within the larger context of machine learning, both in terms of breadth or depth. However, when used as a textbook the instructor could easily address this problem.
4 of 5 people found the following review helpful
4.0 out of 5 stars
Introduction
5 Jun 2008
By Mir
- Published on Amazon.com
Format:Paperback
This is an undergraduate introduction to data mining. The book doesn't go into details. It may be suitable for people who want to get a quick feel of the data mining field. People who need more details shall read more serious and comprehensive introductions. Overall I am giving 4 stars, because I liked it.
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4.8 out of 5 stars
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