Principles of Data Mining

By Max Bramer

Introduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Na?ve 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.

• Format: Paperback
• Language: English
• ISBN: 1447174925
• ISBN13: 9781447174929
• Release Date: May 2020
• Publisher: Springer
• Length: 571 Pages
• Weight: 1.79 lbs.
• Dimensions: 1.2" x 6.1" x 9.2"