Principles of Data Mining

  • Max Bramer

Part of the Undergraduate Topics in Computer Science book series (UTICS)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Max Bramer
    Pages 1-8
  3. Max Bramer
    Pages 9-19
  4. Max Bramer
    Pages 93-119
  5. Max Bramer
    Pages 121-136
  6. Max Bramer
    Pages 137-156
  7. Max Bramer
    Pages 189-208
  8. Max Bramer
    Pages 209-220
  9. Max Bramer
    Pages 221-236
  10. Max Bramer
    Pages 237-251
  11. Max Bramer
    Pages 253-269
  12. Max Bramer
    Pages 311-328
  13. Max Bramer
    Pages 329-343
  14. Max Bramer
    Pages 345-378
  15. Back Matter
    Pages 427-526

About this book


This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering.

Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail.

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 general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers 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.

This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.


Associate Rule Mining Attribute Selection Classification Classifiers Clustering Data Mining Datasets Decision Trees Entropy

Authors and affiliations

  • Max Bramer
    • 1
  1. 1.University of Portsmouth School of ComputingPortsmouthUnited Kingdom

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London Ltd. 2016
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-7306-9
  • Online ISBN 978-1-4471-7307-6
  • Series Print ISSN 1863-7310
  • Series Online ISSN 2197-1781
  • Buy this book on publisher's site