Published by Springer-Verlag. 2013. 440 pages.
ISBN: 978-1-4471-4883-8 (Print) ISBN: 978-1-4471-4884-5
(Online)
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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.
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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.
- 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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Errata
| Chap |
Page |
Line |
Change |
| 13 |
208 |
4 to 5 |
Change http://www.ics.uci.edu/mlearn/MLRepository.html
to http://www.ics.uci.edu/~mlearn/MLRepository.html
|
| 15 |
236 |
-5 to -4 |
Change http://www.ics.uci.edu/mlearn/MLRepository.html
to http://www.ics.uci.edu/~mlearn/MLRepository.html |
| 15 |
227 |
-10 |
'the 1% value' should read 'the 10% value' |
| 15 |
227 |
-10 to -9 |
'We can safely reject the null hypothesis' should read 'We can safely
accept the null hypothesis' |
| 18 |
290 |
-9 |
'download' should be 'downward' |
| 18 |
300 |
-5 |
Final paragraph. This should start: 'We have found two frequent itemsets
ending with item p: {p} and {c, p}' |
| Index |
437 |
-11 (left-hand column) |
Change 'Global Infomation Partition' to 'Global Information Partition' |
Last updated October 30th 2016
Software
These web-based programs are provided to support some of the material in Principles
of Data Mining (second edition)
Calculation of Performance
Measures (Chapter 12)
Comparing Classifiers: Calculation
of Paired t Statistic (Chapter 15)
Calculation of Interestingness
Measures (Section 17.9)
FP-growth Frequent Pattern Trees
Algorithm (Chapter 18)
Datasets
Downloadable copies of datasets referred
to in the book (all in Inducer format)
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