Cluster Analysis and Data Mining ─ An Introduction

Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. This book is applicable to either a course on clustering and classification or as a companion text for a first class in applied statistics. Features: Puts emphasis on illustrating the underlying logic in making decisions during the cluster analysis Brings out the related applications of statistics: Ward s method (ANOVA), JAN(Regression Analysis & Correlational Analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.) Includes separate chapters on JAN and the clustering of categorical dataBrief 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.