Principles of Data Mining

by Max Bramer

Introduction to Data Mining.- Data for Data Mining.- Introduction to Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Using Entropy for Attribute Selection.- Decision Tree 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.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data Time-dependent Data.- An Introduction to Neural Networks.- 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.

588 pages, Paperback

First published March 28, 2007

Reviews

[Johan Decorte · Rating 4 out of 5]

Great introduction to the most important data mining algorithms for someone with a mathematical background.