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Principles of Data Mining

  • Textbook
  • © 2007

Overview

Authors:
  1. Max Bramer

    1. Digital Professor of Information Technology, University of Portsmouth, UK

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  • 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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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xix
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  2. Data for Data Mining

    Pages 11-21

  3. Introduction to Classification: Na¨ive Bayes and Nearest Neighbour

    Pages 23-39

  4. Using Decision Trees for Classification

    Pages 41-50

  5. Decision Tree Induction: Using Entropy for Attribute Selection

    Pages 51-64

  6. Decision Tree Induction: Using Frequency Tables for Attribute Selection

    Pages 65-77

  7. Estimating the Predictive Accuracy of a Classifier

    Pages 79-92

  8. Continuous Attributes

    Pages 93-118

  9. Avoiding Overfitting of Decision Trees

    Pages 119-134

  10. More About Entropy

    Pages 135-154

  11. Inducing Modular Rules for Classification

    Pages 155-171

  12. Measuring the Performance of a Classifier

    Pages 173-185

  13. Association Rule Mining I

    Pages 187-201

  14. Association Rule Mining II

    Pages 203-219

  15. Clustering

    Pages 221-238

  16. Text Mining

    Pages 239-253

  17. Back Matter

    Pages 255-343
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Keywords

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.

Authors and Affiliations

  • Digital Professor of Information Technology, University of Portsmouth, UK

    Max Bramer

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