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. 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. [Note:The companion disc files are available with Amazon proof of purchase by contacting [email protected].]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., Wards 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.[Note:The companion disc files are available with Amazon proof of purchase from [email protected].]

Brief 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.

On the Companion Disc!
[Note:The companion disc files are available with Amazon proof of purchase from [email protected].]
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

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.

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9781938549380

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Mercury Learning and Information

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Ronald S. King

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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.--California Bookwatch (6/1/2015 12:00:00 AM) --This text refers to the paperback edition.

Review

"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." -- California Bookwatch Published On: 2015-06-01 --This text refers to the paperback edition.

Cluster Analysis and Data Mining: An Introduction

Data Analysis

Data Mining

Ronald S. King

Mercury Learning and Information

Ronald S. King

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