Principles of Data Mining (third edition)

Published by Springer-Verlag. 2016. 526 pages. ISBN: 978-1-4471-7306-9 (Print) ISBN: 978-1-4471-7307-6 (Online)

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.

The second edition expanded on the first 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.

This 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 concpt drift.

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.

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None known as at January 7th 2017.


These web-based programs are provided to support some of the material in Principles of Data Mining (third edition)

Calculation of performance measures (Chapter 12)

Calculation of interestingness measures (Section 17.9)

FP-growth Frequent Pattern Trees algorithm (Chapter 18)

Comparing Classifiers: Calculation of paired t statistic (Chapter 15)


Downloadable copies of datasets referred to in the book (all in Inducer format)