Recent Developments in Clustering and Data Analysis (eBook)

Developpements Recents en Classification Automatique et Analyse des Donnees: Proceedings of the Japanese-French Scientific Seminar March 24-26, 1987

Edwin Diday, Chikio Hayashi, Michel Jambu (Herausgeber)

eBook Download: PDF

2014 | 1. Auflage
468 Seiten
Elsevier Science (Verlag)
9781483263090 (ISBN)

Infos

Recent Developments in Clustering and Data Analysis presents the results of clustering and multidimensional data analysis research conducted primarily in Japan and France. This book focuses on the significance of the data itself and on the informatics of the data. Organized into four sections encompassing 35 chapters, this book begins with an overview of the quantification of qualitative data as a method of analyzing statistically multidimensional data. This text then examines the rules of interpretation of correspondence cluster analysis by selecting classes and explaining variables involved in the algorithm of hierarchical classification. Other chapters consider the bootstrap and cross-validation methods, which are applied to the logistic ad nonparametric regression analyses of ordered categorical responses. The final chapter deals with a simpler treatment to classify the sleep state. This book is a valuable resource for researchers and workers in the fields from the behavioral sciences, biological sciences, medicine, and industrial sciences.

Inhalt

Front Cover 1
Recent Developments in Clustering and Data Analysis: Développements Récents en Classification Automatique et Analyse des Données 4
Copyright Page 5
Table of Contents 6
Contributors 10
Preface 14
Section 1: Data Analysis Techniques and Related Topics with Statistical Software 18
CHAPTER 1. NEW DEVELOPMENTS IN MULTIDIMENSIONAL DATA ANALYSIS 20
I. INTRODUCTION 20
II. BIRTH OF QUANTIFICATION OF QUALITATIVE DATA 21
III. OVERVIEW OF QUANTIFICATION METHODS 28
IV. FUTURE PROBLEMS 31
REFERENCES 32
CHAPTER 2. INTERPRETATION OF SOME DATA ANALYSIS METHODS 34
I. INTRODUCTION 34
II. CORRESPONDENCE CLUSTER ANALYSIS 34
III. RESULTS 46
V. CONCLUSION 51
REFERENCES 52
CHAPTER 3. 
54 
I. INTRODUCTION 54
II. SOME USEFUL DISTANCES 55
III. GENERAL ASSOCIATION INDICES 59
IV. SYNTHETIC RELATIONSHIP METHOD 62
V. SYNTHETIC CONCLUSIONS 63
REFERENCES 64
CHAPTER 4. 
66 
I. INTRODUCTION 66
II. DATA AND MODEL 67
III. NONPARAMETRIC REGRESSION 70
IV. LOGISTIC REGRESSION ANALYSIS 73
V. RESIDUAL ANALYSIS 76
VI. CONCLUDING REMARKS 79
ACKNOWLEDGEMENTS 81
REFERENCES 81
CHAPTER 5. 
84 
I. GENERAL VIEW OF THE SOFTWARE 84
II. DESIGN OF THE SOFTWARE: MODULARITY 85
III. EASY SELECTION OF RELEVANT DATA (PROC SELEC) 87
IV. SAVINGS IN THE COMPUTATIONAL STEP (PROC CORMU) 88
V. SPECIFIC TOOLS FOR CLUSTERING (PROC RECIP) 90
VI. AUTOMATIC CHARACTERIZATION OF CLASSES (PROC DECLA) 91
VII. THE GRAPHICAL TOOLS (PROC GRAPH) 92
VIII. CREATING AND COPYING VARIABLES (PROC ESCAL) 93
IX. EXTENSIONS OF THE SOFTWARE 93
REFERENCES 94
CHAPTER 6. 
96 
I. INTRODUCTION 96
II. GRAPHICAL REPRESENTATION AND A TEST STATISTIC 97
III. CALCULATION OF THE EXACT PERCENT POINTS 99
IV. COMPARISONS OF THE POWER AMONG T1, T2, T3, W AND Lw 101
REFERENCES 102
CHAPTER 7. 
104 
I. INTRODUCTION 104
II. GRAPHICAL REPRESENTATION OF RANKS 104
III. GRAPHICAL TEST OF RANKS 107
IV. EXAMPLE 108
V. DISCUSSION 110
SUMMARY 112
APPENDIX 112
REFERENCES 113
CHAPTER 8. 
114 
I. INTRODUCTION 114
II. DEFINITIONS AND BASIC CONCEPTS 115
III. NONLINEAR EXTENSIONS OF LINEAR METHODS 116
IV. INTERPRETATION OF QUANTIFICATION METHODS 118
REFERENCES 119
CHAPTER 9. 
120 
I. INTRODUCTION 120
II. OPTIMUM STRATIFICATION BASED ON A CONCOMITANT VARIABLE 121
III. ROBUSTNESS ON A REGRESSION FUNCTION AND THE CONSTANT C 122
IV. SOME NUMERICAL EXAMPLE 123
REFERENCES 125
CHAPTER 10. 
126 
I. INTRODUCTION 126
II. ELIMINATION OF NON-UNIQUENESS 127
III. USE OF ESTIMABLE FUNCTIONS OF PARAMETERS 131
REFERENCES 132
Section 2: Automatic Classification and Related Techniques 134
CHAPTER 11. 
136 
I. INTRODUCTION 136
II. CLASSIFICATION SPACE AND REPRESENTATION SPACE 138
III. LEARNING HIERARCHICAL CLUSTERING FROM EXAMPLES 144
IV. NEW KIND.S OF GRAPHICAL REPRESENTATION IN CLUSTERING 147
REFERENCES 152
CHAPTER 12. 
154 
I. INTRODUCTION 154
II. MODELS AND THEIR FITTING 155
III. STRATEGIES OF DATA ANALYSIS 162
REFERENCES 165
CHAPTER 13. 
168 
I.- INTRODUCTION 168
II.- CONTINUOUS APPROACH OF AN ULTRAMETRIC 169
III.- CONTINUOUS APPROACH OF AN ADDITIVE TREE METRIC 176
IV.- AN EXAMPLE IN ECOLOGY 178
V.- CONCLUSION 186
REFERENCES 186
CHAPTER 14. 
188 
I. INTRODUCTION 188
II. MODEL 189
III. METHOD 190
IV. ALGORITHM 192
V. EXAMPLE 193
REFERENCES 195
CHAPTER 15. 
196 
I. INTRODUCTION 196
II. DERMAPTERAN FOSSILS 196
III. DERMAPTERAN BIOGEOGRAPHY 197
IV. DERMAPTERAN MULTIVARIATE MORPHOMETRICS 197
V. DERMAPTERAN PHYLOGENETIC AND CLADISTIC INFORMATION 200
VI. DERMAPTERAN PHYSICAL TAXONOMY AND FUTURE CLASSIFICATION 201
REFERENCES 201
CHAPTER 16. 
204 
I. INTRODUCTION REPRESENTATION OF RELATIONAL VARIABLES
II. COMPARING RELATIONAL VARIABLES 208
REFERENCES 215
CHAPTER 17. 
218 
I. INTRODUCTION 218
II. GRAPHICAL REPRESENTATION IN AUTOMATIC CLASSIFICATION 219
III. CONCEPT OF COMPUTER GRAPHICS SYSTEM IN AUTOMATIC CLASSIFICATION 220
IV. CASE STUDIES 224
V. CONCLUSION 237
ACKNOWLEDGEMENTS 238
REFERENCES 238
CHAPTER 18. 
240 
I. INTRODUCTION 240
II. MATERIALS AND METHODS 241
III. EXPERIMENTAL RESULTS 241
IV. DISCUSSION 243
REFERENCES 245
Section 3: Scaling Method and Correspondence Analysis from the Viewpoint of Practical Approach 246
CHAPTER 19. 
248 
I. INTRODUCTION 248
II. CORRESPONDENCE ANALYSIS AS AN APPROXIMATION OF THE DATA MATRIX 249
III. DETERMINING THE NUMBER OF AXES TO BE RETAINED BY CROSS VALIDATION 253
IV. TESTS ON THE SUM OF THE NON-RETAINED LATENT ROOTS 254
REFERENCES 256
CHAPTER 20. 
258 
I. INTRODUCTION 258
II. SINGULAR VALUE DECOMPOSITION 259
III. ALGORITHM AND CRITERIA 264
IV. NESTED CONFIGURATION AND INTERPRETATION 266
V. SOME APPLICATIONS 269
REFERENCES 273
CHAPTER 21. 
276 
I. INTRODUCTION 276
II. MATHEMATICAL PREPARATIONS 277
III. PARTIAL CORRESPONDENCE ANALYSIS 279
IV. NUMERICAL EXAMPLE OF PARTIAL CORRESPONDENCE ANALYSIS 281
REFERENCES 283
CHAPTER 22. 
284 
I. INTRODUCTION 284
II. OPTIMAL SCORING METHOD MAXIMIZING CANONICAL CORRELATION COEFFICIENT 285
III. OPTIMAL SCORING METHOD FOR THREE-WAY QUALITATIVE DATA 287
IV. EXAMPLES FOR THREE-WAY QUALITATIVE DATA 290
REFERENCES 296
CHAPTER 23. 
298 
I. INTRODUCTION 298
II. QUANTIFICATION I 299
III. QUANTIFICATION II 303
IV. NUMERICAL INVESTIGATION 305
V. DISCUSSION 308
REFERENCES 310
CHAPTER 24. CONVERSATIONAL DATA ANALYSIS SYSTEM 312
I. INTRODUCTION 312
II. PRELIMINARY ANALYSIS OF MULTIVARIATE DATA 313
III. CONVERSATIONAL SELECTION OF VARIABLES AND ITEMS 316
IV. REGRESSION DIAGNOSIS AND ANALYSIS OF RESIDUAL 319
V. POOLING OF CATEGORIES 320
VI. CONVERSATIONAL PROCESSING AND BATCH PROCESSING 321
CHAPTER 25. 
324 
I. INTRODUCTION 324
II. MULTIPLE FACTOR ANALYSIS 325
III. ANALYSIS OF FREQUENCY TABLES 335
CONCLUSION 337
REFERENCE 338
CHAPTER 26. 
340 
I. INTRODUCTION 340
II. CROSS-TABULATION AS A PROCEDURE OF DATA ANALYSIS 341
III. DESIGNING ANALYSIS 345
Section 4: Applications: Extraction and Interpretation of Information in Multidimensional Data 346
CHAPTER 27. 
348 
I. THE INITIAL PROBLEMS 348
II. PRESENTATION OF THE DATA SET 349
III. PRESENTATION OF THE METHODS 349
IV. THE FUNDAMENTAL NOTION OF ACTIVE VARIABLE (AV) 349
V. THE "SWARM" OF AV AND THE "GRID" OF SV CATEGORIES 351
VI. CHANGES IN THE PATTERNINGS OF OPINIONS 356
VII. REMARKS : Existence and autonomy of structures 358
REFERENCES 359
CHAPTER 28. 
360 
I. FORWORD 360
II. METHODS OF ANALYSIS 362
III. Application Examples 369
IV. CONCLUSION 388
REFERENCES 388
CHAPTER 29. DATA ANALYTIC APPROACHES TO HUMAN BEHAVIORAL RELATIONSHIPS IN A SURVEY OF ACCIDENTS 390
I. INTRODUCTION 390
II. METHOD 391
III. RESULTS AND DISCUSSION 391
ACKNOWLEDGEMENTS 397
REFERENCE 397
CHAPTER 30. 
398 
I. THE STATISTICAL MODEL OF EVENTS FORECAST 398
II. ATTENUATION COEFFICIENT OF THE MAHALANOBIS DISTANCE 401
III. THE DISTRIBUTION LAW OF THE MAHALANOBIS DISTANCE 404
IV. NUMERICAL STABILITY OF THE FORECASTING MODEL 406
V. STATISTICAL STABILITY OF THE FORECASTING MODEL 408
VI. THE NATURAL LINK TO OVEREVALUATE THE NUMBER OF PREDICTORS 414
REFERENCES 415
CHAPTER 31. 
418 
I. INTRODUCTION 418
II. QUANTIFICATION THEORY TYPE III AND ONE DIMENSIONAL SCALING STRUCTURE 418
III. APPLICATION 425
IV. CONCLUSION 426
REFERENCES 428
CHAPTER 32. 
430 
I. INTRODUCTION 430
II. ILLUSTRATIVE BINARY DECISION TREE 431
III. CONSTRUCTION OF A BINARY DECISION TREE 431
IV. PRUNING PROCESS 435
V. CHOOSING THE RIGHT-SIZED TREE : A PROBLEM OF DETERMINATION OF THE MOST RELIABLE ESTIMATED TRUE ERROR RATE 437
VI. CONCLUSION 441
REFERENCES 441
CHAPTER 33. 
442 
I. INTRODUCTION 442
II. THEORETICAL CONSIDARATION 443
III. EXPERIMENT 448
IV. CONCLUSION 452
REFERENCES 452
CHAPTER 34. 
454 
I. INTRODUCTION 454
II. SPIKE DETECTION 454
III. FETAL QRS DETECTION 457
IV. DISCUSSION 459
V. CONCLUSION 461
ACKNOWLEDGEMENT 461
REFERENCES 461
CHAPTER 35. 
462 
I. MICRO STAGE AGGREGATION PROCEDURE 462
II. MACRO ANALYSIS 466
REFERENCE 469

Produkt-Details

• Erscheint lt. Verlag: 10.5.2014
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Algebra
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Technik
• ISBN-13: 9781483263090 / 9781483263090