Overview
- Authors:
- Max Bramer
Digital Professor of Information Technology, University of Portsmouth, UK
Search author on:PubMed Google Scholar
- Presents the principal techniques of data mining with particular emphasis on explaining and motivating the techniques used
- Focuses on developing an understanding of the basic algorithms and an awareness of their strengths and weaknesses
- Readers are not required to have a strong mathematical or statistical background
- Can be used as a textbook and also for self-study
- Includes supplementary material:sn.pub/extras
Part of the book series:Undergraduate Topics in Computer Science (UTICS)
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About this book
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 and explanations of the algorithms given. This should prove of value to readers of all kinds, from those whose only use of data mining techniques will be via commercial packages right through to academic researchers.
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 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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Discover the latest articles, books and news in related subjects.Table of contents (15 chapters)
Front Matter
Pages i-xixData for Data Mining
Pages 11-21Using Decision Trees for Classification
Pages 41-50Continuous Attributes
Pages 93-118Avoiding Overfitting of Decision Trees
Pages 119-134More About Entropy
Pages 135-154Inducing Modular Rules for Classification
Pages 155-171Measuring the Performance of a Classifier
Pages 173-185Association Rule Mining I
Pages 187-201Association Rule Mining II
Pages 203-219Clustering
Pages 221-238Text Mining
Pages 239-253Back Matter
Pages 255-343
Authors and Affiliations
Digital Professor of Information Technology, University of Portsmouth, UK
Max Bramer
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Bibliographic Information
Book Title:Principles of Data Mining
Authors:Max Bramer
Series Title:Undergraduate Topics in Computer Science
DOI:https://doi.org/10.1007/978-1-84628-766-4
Publisher:Springer London
eBook Packages:Computer Science,Computer Science (R0)
Copyright Information:Springer-Verlag London 2007
eBook ISBN:978-1-84628-766-4Published: 06 March 2007
Series ISSN: 1863-7310
Series E-ISSN: 2197-1781
Edition Number:1
Number of Pages:X, 344
Number of Illustrations:200 b/w illustrations
Topics:Data Structures and Information Theory,Theory of Computation,Information Storage and Retrieval,Database Management,Artificial Intelligence,Programming Techniques