Introduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve 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.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- 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.
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