Cluster Analysis and Data Mining: An Introduction
Language: English
Published by Mercury Learning and Information, 2014
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- Title
- Cluster Analysis and Data Mining: An Introduction
- Author
- King, Ronald S.
- Publisher
- Mercury Learning and Information
- Publication year
- 2014
- Condition
- Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1938549384
- ISBN 13
- 9781938549380
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. This book is applicable to either a course on clustering and classification or as a companion text for a first class in applied statistics.
Features:
Puts emphasis on illustrating the underlying logic in making decisions during the cluster analysis
Brings out the related applications of statistics: Ward s method (ANOVA), JAN(Regression Analysis & Correlational Analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.)
Includes separate chapters on JAN and the clustering of categorical data
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.
Features:
Puts emphasis on illustrating the underlying logic in making decisions during the cluster analysis
Brings out the related applications of statistics: Ward s method (ANOVA), JAN(Regression Analysis & Correlational Analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.)
Includes separate chapters on JAN and the clustering of categorical data
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.
"Synopsis" may belong to another edition of this title.
About the Author
Spanning a career of four decades of teaching and administration at multiple universities, Dr. RONALD S. KING 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 and he currently teaches online courses for Tarleton State University.
"About the title" may belong to another edition of this title.
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