Cluster Analysis and Data Mining: An Introduction

by Ronald S. King

Overview

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.

eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [e-mail].

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.

Product Details

ISBN-13:	9781938549380
Publisher:	Mercury Learning & Information
Publication date:	11/30/2014
Pages:	300
Product dimensions:	7.00(w) x 8.90(h) x 0.80(d)

About the Author

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.

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

Editorial 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."

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