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Cluster Analysis for Applications
Probability and Mathematical Statistics: A Series of Monographs and Textbooks
- 1st Edition - May 10, 2014
- Author: Michael R. Anderberg
- Language: English
- Paperback ISBN:9 7 8 - 1 - 4 8 3 1 - 7 5 5 8 - 4
- eBook ISBN:9 7 8 - 1 - 4 8 3 1 - 9 1 3 9 - 3
Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among… Read more
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Request a sales quoteCluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis. Comprised of 10 chapters, this book begins with an introduction to the subject of cluster analysis and its uses as well as category sorting problems and the need for cluster analysis algorithms. The next three chapters give a detailed account of variables and association measures, with emphasis on strategies for dealing with problems containing variables of mixed types. Subsequent chapters focus on the central techniques of cluster analysis with particular reference to computational considerations; interpretation of clustering results; and techniques and strategies for making the most effective use of cluster analysis. The final chapter suggests an approach for the evaluation of alternative clustering methods. The presentation is capped with a complete set of implementing computer programs listed in the Appendices to make the use of cluster analysis as painless and free of mechanical error as is possible. This monograph is intended for students and workers who have encountered the notion of cluster analysis.
Preface
Acknowledgements
Chapter 1. The Broad View of Cluster Analysis
1.1 Category Sorting Problems
1.2 Need for Cluster Analysis Algorithms
1.3 Uses of Cluster Analysis
1.4 Literature of Cluster Analysis
1.5 Purpose of This Book
Chapter 2. Conceptual Problems in Cluster Analysis
2.1 Elements of a Cluster Analysis
2.2 Illustrative Example
2.3 Some Philosophical Observations
2.4 A Note on Optimality and Intuition
Chapter 3. Variables and Scales
3.1 Classification of Variables
3.2 Scale Conversions
3.3 The Application of Scale Conversions
Chapter 4. Measures of Association among Variables
4.1 Measures between Ratio and Interval Variables
4.2 Measures between Nominal Variables
4.3 Measures between Binary Variables
4.4 Strategies for Mixed Variable Data Sets
Chapter 5. Measures of Association among Data Units
5.1 Metric Measures for Interval Variables
5.2 Nonmetric Measures for Interval Variables
5.3 Measures Using Binary Variables
5.4 Measures Using Nominal Variables
5.5 Mixed Variable Strategies
Chapter 6. Hierarchical Clustering Methods
6.1 The Central Agglomerative Procedure
6.2 The Stored Matrix Approach
6.3 The Stored Data Approach
6.4 The Sorted Matrix Approach
6.5 Other Approaches
Chapter 7. Nonhierarchical Clustering Methods
7.1 Initial Configurations
7.2 Nearest Centroid Sorting—Fixed Number of Clusters
7.3 Nearest Centroid Sorting—Variable Number of Clusters
7.4 Other Approaches to Nonhierarchical Clustering
Chapter 8. Promoting Interpretation of Clustering Results
8.1 Aids to Interpreting Hierarchical Classifications
8.2 An Aid to Interpreting a Partition of Data Units into Clusters
Chapter 9. Strategies for Using Cluster Analysis
9.1 Sequential Clustering of Data Units
9.2 Complementary Use of Several Clustering Methods
9.3 Cluster Analysis as an Adjunct to Other Statistical Methods
9.4 Clustering with Respect to an External Criterion
9.5 The Need for Research on Strategies
Chapter 10. Comparative Evaluation of Cluster Analysis Methods
10.1 An Approach to the Evaluation of Clustering Methods
10.2 Quantitative Assessment of Performance for Clustering Methods
10.3 List of Candidate Characteristic for Problems and Methods
10.4 The Evaluation Task Lying Ahead
Appendix A. Correlation and Nominal Variables
A.1 The Fundamental Analysis
A.2 The Problem of Isolated Cells
A.3 Deflating the Squared Correlation
Appendix B. Programs for Scale Conversions
B.1 Partitions of the Truncated Normal Distribution
B.2 Iterative Improvement of a Partition
Program CUTS
Function ERF
Program DIVIDE
Subroutine TEST
Subroutine SORT
Function PSUMSQ
Appendix C. Programs for Association Measures among Nominal and Interval Variables
C.1 General Design Features
C.2 Deck Setup and Utilization
Subroutine GCORR
Subroutine INPTR
Subroutine NCAT
Subroutine EIGEN
Subroutine VSORT
Function CORXX
Function CORKX
Function CORKK
Appendix D. Programs for Association Measures Involving Binary Variables
D.1 Bit-Level Storage
D.2 Computing Association Measures
D.3 Use of the Program
Program BINARY
Subroutine BDATA
Function Subprogram KOUNT
Function BASSN
Appendix E. Programs for Hierarchical Cluster Analysis
E.1 Stored Similarity Matrix Approach
E.2 Stored Data Approach
E.3 Sorted Matrix Approach
Subroutine CNTRL
Subroutine CLSTR
Function LFIND
Subroutine METHOD
Subroutine MANAGE
Subroutine GROUP
Subroutine PROC
Subroutine ALLINI
Subroutine PREP
Appendix F. Programs for Nonhierarchical Clustering
Subroutine EXEC
Subroutine RESULT
Subroutine KMEAN
Appendix G. Programs to Aid Interpretation of Clustering Results
G.1 A Program for Manipulating Hierarchical Trees
G.2 Permuting the Similarity Matrix
G.3 Error Sum of Squares Analysis
G.4 Analysis of a Given Partition
Subroutine DETAIL
Subroutine READCM
Subroutine TREE
Program PERMUTE
Subroutine MTXIN
Function LFIND
Program ERROR
Program POSTDU
Appendix H. Relations Among Cluster Analysis Programs
References
Index
- No. of pages: 376
- Language: English
- Edition: 1
- Published: May 10, 2014
- Imprint: Academic Press
- Paperback ISBN: 9781483175584
- eBook ISBN: 9781483191393
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