Principles of Data Mining

Authors:

ISBN: 978-1-4471-4883-8 (Print) 978-1-4471-4884-5 (Online)

Table of contents (20 chapters)

  1. Front Matter

    Pages I-XIV

  2. No Access

    Chapter

    Pages 1-8

    Introduction to Data Mining

  3. No Access

    Chapter

    Pages 9-19

    Data for Data Mining

  4. No Access

    Chapter

    Pages 21-37

    Introduction to Classification: Naïve Bayes and Nearest Neighbour

  5. No Access

    Chapter

    Pages 39-48

    Using Decision Trees for Classification

  6. No Access

    Chapter

    Pages 49-62

    Decision Tree Induction: Using Entropy for Attribute Selection

  7. No Access

    Chapter

    Pages 63-78

    Decision Tree Induction: Using Frequency Tables for Attribute Selection

  8. No Access

    Chapter

    Pages 79-92

    Estimating the Predictive Accuracy of a Classifier

  9. No Access

    Chapter

    Pages 93-119

    Continuous Attributes

  10. No Access

    Chapter

    Pages 121-136

    Avoiding Overfitting of Decision Trees

  11. No Access

    Chapter

    Pages 137-156

    More About Entropy

  12. No Access

    Chapter

    Pages 157-174

    Inducing Modular Rules for Classification

  13. No Access

    Chapter

    Pages 175-187

    Measuring the Performance of a Classifier

  14. No Access

    Chapter

    Pages 189-208

    Dealing with Large Volumes of Data

  15. No Access

    Chapter

    Pages 209-220

    Ensemble Classification

  16. No Access

    Chapter

    Pages 221-236

    Comparing Classifiers

  17. No Access

    Chapter

    Pages 237-251

    Association Rule Mining I

  18. No Access

    Chapter

    Pages 253-269

    Association Rule Mining II

  19. No Access

    Chapter

    Pages 271-309

    Association Rule Mining III: Frequent Pattern Trees

  20. No Access

    Chapter

    Pages 311-328

    Clustering

  21. No Access

    Chapter

    Pages 329-343

    Text Mining

  22. Back Matter

    Pages 345-440