Principles of Data Mining (Paperback, 4th ed. 2020)

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.- 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 II: 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.