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Undergraduate Topics in Computer Science

Principles of Data Mining

Authors: Bramer, Max

  • Presents the principal techniques of data mining with particular emphasis on explaining and motivating the techniques used
  • Focuses on understanding of the basic algorithms and awareness of their strengths and weaknesses
  • Useful as a textbook and also for self-study
  • Substantially expanded second edition
  • Each chapter contains practical exercises to enable readers to check their progress, and there is a full glossary of technical terms
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Buy this book

eBook $44.99
price for USA (gross)
  • ISBN 978-1-4471-4884-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
About this Textbook

Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas.

Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail.

This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data.

Principles of Data Mining 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.

Suitable 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.

Reviews

From the reviews of the second edition:

“This book introduces the concept of data mining and explains the various techniques involved. … This book is written primarily as a text for a course on data mining. The rich pedagogical features, including illustrations, examples, solved problems, exercises and solutions, a glossary, and references, make it an ideal choice for that purpose. It would be very useful for any reader who wants to gain a good understanding of data mining concepts and techniques.” (Alexis Leon, Computing Reviews, September, 2013)

Table of contents (20 chapters)

  • Introduction to Data Mining

    Bramer, Max

    Pages 1-8

  • Data for Data Mining

    Bramer, Max

    Pages 9-19

  • Introduction to Classification: Naïve Bayes and Nearest Neighbour

    Bramer, Max

    Pages 21-37

  • Using Decision Trees for Classification

    Bramer, Max

    Pages 39-48

  • Decision Tree Induction: Using Entropy for Attribute Selection

    Bramer, Max

    Pages 49-62

Buy this book

eBook $44.99
price for USA (gross)
  • ISBN 978-1-4471-4884-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
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Bibliographic Information

Bibliographic Information
Book Title
Principles of Data Mining
Authors
Series Title
Undergraduate Topics in Computer Science
Copyright
2013
Publisher
Springer-Verlag London
Copyright Holder
Springer-Verlag London
eBook ISBN
978-1-4471-4884-5
DOI
10.1007/978-1-4471-4884-5
Series ISSN
1863-7310
Edition Number
2
Number of Pages
XIV, 440
Number of Illustrations and Tables
101 b/w illustrations
Topics