
Principles of Data Mining
Max Bramer(Author)
Springer (Publisher)
Published on 26. April 2007
Book
Paperback/Softback
X, 344 pages
978-1-84628-765-7 (ISBN)
Article exhausted; check for reprint
Description
This book explains 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 will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.
More details
Series
Edition
2007
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Product notice
Paperback (trade)
Illustrations
200
200 s/w Abbildungen
1, black & white illustrations
Dimensions
Height: 24.4 cm
Width: 17 cm
Thickness: 18 mm
Weight
1250 gr
ISBN-13
978-1-84628-765-7 (9781846287657)
DOI
10.1007/978-1-84628-766-4
Schweitzer Classification
Other editions
New editions

Max Bramer
Principles of Data Mining
Book
02/2013
2nd Edition
Springer
€48.10
Article exhausted; check for reprint
Additional editions

Content
Data for Data Mining.- to Classification: Na¨ive Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Association Rule Mining I.- Association Rule Mining II.- Clustering.- Text Mining.