R.S. King - Cluster Analysis and Data Mining: An Introduction

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R.S. King. Cluster Analysis and Data Mining: An Introduction

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To the memory of my father, who made it possible and to LaJuan, the shining light in my life who survived the process.

On the DVD

Appendix A: Clustering Analysis with SPSS

Appendix B: Clustering Analysis with SAS

Appendix C: Neymann-Scott Cluster Generator Program Listing

Appendix D: Janceys Clustering Program Listing

Appendix E: JAN Program

Appendix F: UCI Machine Learning Depository KD Nuggets Data Sets

Appendix G: Free Statistics Software (Calculator)

Appendix H: Solutions to Odd Exercises

T his book is appropriate for a first course in clustering methods and data mining. Clustering and data mining methods are applicable in many fields of study, for example:

in the life sciences for developing complete taxonomies,

in the medical sciences for discovering more effective and economical means for making positive diagnosis in the treatment of patients,

in the behavioral and social sciences for discerning human judgments and behavior patterns,

in the earth sciences for identifying and classifying geographical regions,

in the engineering sciences for pattern recognition and artificial intelligence applications, and

in decision and information sciences for analysis of markets and documents.

The first five chapters consider early historical clustering methods. Chapters . JAN allows for numerical and categorical variables to be included in a clustering study.

All of the methods in Chapters , capture partitions plus modified versions for the partitions. The modified partitions allow for overlapping clusters.

is an introduction to the data mining topics of classification and association rules, which enable qualitative rather than simply quantitative data mining studies to be conducted.

Cluster analysis is essentially an art, but can be accomplished scientifically if the results of a clustering study can be validated. This is discussed in . Determination of the validity of individual clusters and the validation of a clustering, or collection of clusters, are discussed.

surveys a variety of algorithms for clustering categorical data: ROCK, STIRR, CACTUS, and CLICK. These methods are dependent on underlying data structures and are applicable to relational databases.

Applications of clustering methods are presented in Chapters is an overview of model-based clustering. This is often used in physical science research studies for data generation.

A summary of the issues and trends in the cluster analysis field is made in . Besides giving recommendations for further study, an introduction to neural networks is presented. The appendices provide a variety of resources (software, URLs, algorithms, references) for the cluster analysis plus URLs for test data files.

The text is applicable to either a course on clustering, data mining, and classification or as a companion text for a first class in applied statistics. Clustering and data mining are good motivators and applications of the topics commonly included in an introductory applied statistics course.

The scheduling references for each of the chapters, in an applied statistics class, could be as follows: