http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource (xiii, 359 pages) illustrations
Front Cover; Cluster Analysis for Applications; Copyright Page; Dedication; Table of Contents; PREFACE; ACKNOWLEDGMENTS; CHAPTER I. 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 Conversions3.3 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 Procedure6.2 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 ANALYSIS9.1 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 Lists of Candidate Characteristics for Problems and Methods; 10.4 The Evaluation Task Lying Ahead
APPENDIX A: CORRELATION AND NOMINAL VARIABLESA. 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; APPENDIX C: PROGRAMS FOR ASSOCIATION MEASURES AMONG NOMINAL AND INTERVAL VARIABLES; C.1 General Design Features; C.2 Deck Setup and Utilization; 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
Originally presented as the author's thesis, University of Texas at Austin, 1971.
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Includes bibliographical references (pages 347-353).
Classification automatique (Statistique)
Anderberg, Michael R. (Michael Rex), 1941-
Probability and mathematical statistics ; 19