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| Data Mining: Introductory and Advanced Topics
Paperback
Edition: 1
Author: Margaret H. Dunham
Publisher: Prentice Hall
Release Date: 2002-09-01
ISBN-10: 0130888923
ISBN-13: 9780130888921
List Price: $90.67
Average Customer Rating:
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Summaries and Customer Reviews are supplied by Amazon.com
Margaret Dunham offers the experienced data base professional or graduate level Computer Science student an introduction to the full spectrum of Data Mining concepts and algorithms. Using a database perspective throughout, Professor Dunham examines algorithms, data structures, data types, and complexity of algorithms and space. This text emphasizes the use of data mining concepts in real-world applications with large database components. KEY FEATURES: *Covers advanced topics such as Web Mining and Spatial/Temporal mining *Includes succinct coverage of Data Warehousing, OLAP, Multidimensional Data, and Preprocessing *Provides case studies *Offers clearly written algorithms to better understand techniques *Includes a reference on how to use Prototypes and DM products
Good introductory survey to various technologies
| Customer Rating:  | | 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. |
clarity in exposition
| Customer Rating: | 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. |
Good book for those interested in Data Mining, Machine Learn
| Customer Rating: | 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.
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