Cluster Analysis and Data Mining. An Introduction

Description

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. Designed for training industry professionals or for a course on clustering and classification, it can also be used as a companion text for applied statistics. No previous experience in clustering or data mining is assumed. Informal algorithms for clustering data and interpreting results are emphasized. In order to evaluate the results of clustering and to explore data, graphical methods and data structures are used for representing data. Throughout the text, examples and references are provided, in order to enable the material to be comprehensible for a diverse audience. A companion disc includes numerous appendices with programs, data, charts, solutions, etc. Features Places emphasis on illustrating the underlying logic in making decisions during the cluster analysis Discusses the related applications of statistic, e.g., Ward’s method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.) Contains separate chapters on JAN and the clustering of categorical data Includes a companion disc with solutions to exercises, programs, data sets, charts, etc. CONTENTS......Page 8 INTRODUCTION TO CLUSTER ANALYSIS......Page 18 OVERVIEW OF DATA MINING......Page 35 HIERARCHICAL CLUSTERING......Page 44 PARTITION CLUSTERING......Page 75 JUDGMENTAL ANALYSIS......Page 98 FUZZY CLUSTERING MODELS AND APPLICATIONS......Page 133 Association Rules......Page 164 CLUSTER VALIDITY......Page 196 Clustering Categorical Data......Page 246 MINING OUTLIERS......Page 275 MODEL-BASED CLUSTERING......Page 292 GENERAL ISSUES......Page 308 INDEX......Page 326