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Cluster analysis for applications

Author: Anderberg, Michael R. Series: Probability and mathematical statistics ; 19 Publisher: Academic Press, 1973.Language: EnglishDescription: 359 p. : Ill. ; 24 cm.ISBN: 0120576503Type of document: BookBibliography/Index: Includes bibliographical references and index
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Asia Campus
Textbook Collection (PhD)
Print QA278 .A63 1973
(Browse shelf)
900192101
Consultation only 900192101
Book Asia Campus
Textbook Collection (PhD)
Print QA278 .A63 1973
(Browse shelf)
900192095
Available 900192095
Total holds: 0

Includes bibliographical references and index

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Cluster Analysis for Applications Probability and Mathematical Statistics Monograph Contents PREFACE ACKNOWLEDGMENTS 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 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 Variables and Scales 3.1 Classification of Variables 3.2 Scale Conversions 3.3 The Application of Scale Conversions 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 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 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 Xi Xiii 1 3 4 6 8 Chapter 2. 10 16 18 24 Chapter 3. 26 30 68 Chapter 4. 71 75 83 92 Chapter 5. 99 110 114 122 127 Chapter 6. 132 134 145 149 152 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 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 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 157 160 167 173 Chapter 8. 177 180 Chapter 9. 182 187 190 194 198 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 200 202 209 213 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 216 221 226 228 229 230 230 231 233 234 234 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 235 236 237 241 242 242 245 246 247 248 259 261 262 262 264 267 269 276 276 277 278 281 283 283 290 293 295 300 303 307 310 311 326 329 330 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 330 330 333 333 337 339 340 340 342 346 References INDEX 347 355

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