Table of Contents

1) Introduction to Cluster Analysis
2) Overview of Data Mining
3) Hierarchical Clustering
4) Partition Clustering
5) Judgmental Analysis
6) Fuzzy Clustering Models and Applications
7) Classification and Association Rules
8) Cluster Validity
9) Clustering Categorical Data
10) Mining Outliers
11) Model-Based Clustering
12) General Issues

Appendices
Index

About the Author

King Ronald S. :

Ronald S. King holds a PhD in applied statistics and currently teaches online courses for Tarleton State University (TX). Spanning a career of four decades of teaching and administration at multiple universities, he brings a unique perspective to the fields of statistics, computer science, and information systems. His lifetime career publications have made numerous contributions to these fields.

Reviews

Cluster Analysis and Data Mining: An Introduction pairs a DVD of appendix references on clustering analysis using SPSS, SAS, and more with a discussion designed for training industry professionals and students, and assumes no prior familiarity in clustering or its larger world of data mining. It provides theories, real-world applications, and pairs these with case histories and examples to support algorithms for clustering data and gathering their results. From different clustering models, their applications, and their uses to exercises and reviews designed to reinforce learning, this is a solid reference for any just beginning to delve into the specifics of data mining operations and options.