9. Modeling for Web Usage Mining: Clustering, Association, and Classification

  1. Zdravko Markov Ph.D.1 and
  2. Daniel T. Larose Ph.D. Professor of Statistics Director2

Published Online: 17 JUL 2006

DOI: 10.1002/9780470108093.ch9

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

How to Cite

Markov, Z. and Larose, D. T. (2007) Modeling for Web Usage Mining: Clustering, Association, and Classification, in Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470108093.ch9

Author Information

  1. 1

    Department of Computer Science, Central Connecticut State University, New Britain, CT, USAwww.cs.ccsu.edu/∼markov/

  2. 2

    Data Mining@CCSU, Department of Mathematical Sciences, Central Connecticut State University, New Britain, CT, USAwww.math.ccsu.edu/larose

Publication History

  1. Published Online: 17 JUL 2006
  2. Published Print: 11 APR 2007

ISBN Information

Print ISBN: 9780471666554

Online ISBN: 9780470108093

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Keywords:

  • web usage mining - clustering, association, and classification;
  • BIRCH clustering algorithm;
  • discretizing numerical variables and binning

Summary

This chapter contains sections titled:

  • Introduction

  • Modeling Methodology

  • Definition of Clustering

  • The BIRCH Clustering Algorithm

  • Affinity Analysis and the A Priori Algorithm

  • Discretizing the Numerical Variables: Binning

  • Applying the A Priori Algorithm to the CCSU Web Log Data

  • Classification and Regression Trees

  • The C4.5 Algorithm

  • References

  • Exercises