Principles of Data Mining (Undergraduate Topics in Computer Science)

By Max Bramer

Book Overview

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. This description may be from another edition of this product.

• Format: Paperback
• Language: English
• ISBN: 1846287650
• ISBN13: 9781846287657
• Release Date: January 2007
• Publisher: Springer
• Length: 343 Pages
• Weight: 1.15 lbs.
• Dimensions: 0.7" x 7.1" x 9.2"