- Paperback: 315 pages
- Publisher: Prentice Hall; 1 edition (22 Aug 2002)
- Language English
- ISBN-10: 0130888923
- ISBN-13: 978-0130888921
- Product Dimensions: 24.1 x 18.3 x 1.8 cm
- Amazon Bestsellers Rank: 1,405,861 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Description
Review
"It is the best book on data mining so far, and I would definitely adopt it for my course. The book is very comprehensive and covers all of the data mining topics and algorithms of which 1 am aware. The depth of coverage of each topic or method is exactly right and appropriate. Each algorithm is presented in pseudocode that is sufficient for any interested readers to convert into a working implementation in a computer language of their choice." — Michael H. Huhns, University of South Carolina
"Discussion on distributed, parallel, and incremental algorithms is outstanding." — Zoran Obradovic, Temple University
Product Description
For courses in data mining.
Thorough in its coverage from basic to advanced topics, this text presents the algorithms and techniques used in data mining. It introduces readers to various data mining concepts and algorithms.
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3 reviews
5 of 6 people found the following review helpful
clarity in exposition
22 Aug 2006
Format:Paperback
Dunham gives a clear explanation of the main ideas in data mining. It's a concise book, directed towards the researcher or programmer. Space considerations meant that some topics are only briefly but succinctly covered, like fuzzy logic.
More details are provided about neural networks, genetic algorithms and similarity measures. Bayesian classifications also get a good mention. Other classification measures involve distance-based methods to define clusters. For clustering, you should note that exactly what goes into a given cluster can be rather subjective. It could depend on your choice of metric.
There is a fair amount of maths. Accessible to someone with a couple of years of university level maths, especially involving linear algebra.
The section on Web mining is especially interesting. The Web is probably the largest database in the world. Certainly the most accessible. But with different characteristics from many other databases. Web data might be wrong, deliberately or otherwise. And some websites might be link farms, that try to pump up page rankings. Other databases simply don't have this concern about their contents. Dunham explains Google's PageRank and a competing idea from IBM.
The algorithms are given in pseudocode. Which should not be a problem to an experienced programmer. Translating these into your choice of language is (or at least it should be) a lesser conceptual task than understanding the methods themselves. Or devising new methods. The book also aids the latter. Dunham's descriptions of the overall logic behind each algorithm is a good lead into what is needed in construction new ones.
More details are provided about neural networks, genetic algorithms and similarity measures. Bayesian classifications also get a good mention. Other classification measures involve distance-based methods to define clusters. For clustering, you should note that exactly what goes into a given cluster can be rather subjective. It could depend on your choice of metric.
There is a fair amount of maths. Accessible to someone with a couple of years of university level maths, especially involving linear algebra.
The section on Web mining is especially interesting. The Web is probably the largest database in the world. Certainly the most accessible. But with different characteristics from many other databases. Web data might be wrong, deliberately or otherwise. And some websites might be link farms, that try to pump up page rankings. Other databases simply don't have this concern about their contents. Dunham explains Google's PageRank and a competing idea from IBM.
The algorithms are given in pseudocode. Which should not be a problem to an experienced programmer. Translating these into your choice of language is (or at least it should be) a lesser conceptual task than understanding the methods themselves. Or devising new methods. The book also aids the latter. Dunham's descriptions of the overall logic behind each algorithm is a good lead into what is needed in construction new ones.
1 of 1 people found the following review helpful
Good introductory survey to various technologies
25 Sep 2007
Format:Paperback|Amazon Verified Purchase
The book serves its purpose of providing a introduction to the various technologies that make up data mining. There are three main topic sections. The first gives an overview of the technologies involved such as fuzzy logic, bayesian probability, and neural networks. The second topic area is more concentrated and focuses on how data mining works. This involves utilizing clustering, association, and classification of data. The final section covers advanced topics in web, spatial, and temporal mining. The only complaint that I would have is that most of the coverage at least in section one is cursory and one needs other reference books for serious work in the field. A very strong feature of the book is that pseudocode algorithms are offered in many sections.
3 of 9 people found the following review helpful
Good book for those interested in Data Mining, Machine Learn
3 Nov 2004
Format:Paperback
Currently I am taking a Machine Learning course. This book is really helpful and intuitive. My friends who are studying Bioinformatics also found it useful.
All algorithms are presented in pseudo-code.
All algorithms are presented in pseudo-code.
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