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Principles of data mining

Author: Max Bramer
Publisher: London : Springer, 2007.
Series: Undergraduate topics in computer science.
Edition/Format:   Print book : EnglishView all editions and formats
Publication:Principles of Data Mining.
Database:WorldCat
Summary:

This book explores 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  Read more...

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Max Bramer
ISBN: 1846287650 9781846287657 1846287669 9781846287664
OCLC Number: 255434561
Description: x, 343 S. : graph. Darst.
Contents: Introduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naive 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.- References.- Appendix A: Essential Mathematics.- Appendix B: Datasets.- Appendix C: Sources of Further Information.- Appendix D: Glossary and Notation.- Appendix E: Solutions to Self-assessment Exercises.- Index.
Series Title: Undergraduate topics in computer science.
Responsibility: Max Bramer.
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