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 info@merclearning.com.]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 en¬able 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.[Note:The companion disc files are available with Amazon proof of purchase from info@merclearning.com.]
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 info@merclearning.com.]
Appendix A: Clustering Analysis with SPSS
Appendix B: Clustering Analysis with SAS
Appendix C: Neymann-Scott Cluster Generator Program Listing
Appendix D: Jancey’s 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.
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.[Note:The companion disc files are available with Amazon proof of purchase from info@merclearning.com.]
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 info@merclearning.com.]
Appendix A: Clustering Analysis with SPSS
Appendix B: Clustering Analysis with SAS
Appendix C: Neymann-Scott Cluster Generator Program Listing
Appendix D: Jancey’s 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.
