Cluster Analysis and Data Mining : : An Introduction / / Ronald S. King.

• VerfasserIn: King, Ronald S.,
• Place / Publishing House: Dulles, VA : : Mercury Learning and Information, , [2013]; ©2013
• Year of Publication: 2013
• Language: English
• Physical Description: 1 online resource (300 p.)

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]*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.

Table of Contents

Frontmatter -- CONTENTS -- Preface -- Chapter 1: Introduction to Cluster Analysis -- Chapter 2: Overview of Data Mining -- Chapter 3: Hierarchical Clustering -- Chapter 4: Partition Clustering -- Chapter 5: Judgmental Analysis -- Chapter 6: Fuzzy Clustering Models and Applications -- Chapter 7: Classification and Association Rules -- Chapter 8: Cluster Validity -- Chapter 9: Clustering Categorical Data -- Chapter 10: Mining Outliers -- Chapter 11: Model-based Clustering -- Chapter 12: General Issues -- INDEX