Data Mining: A Heuristic Approach

9781591400110, 1-59140-011-2, 1-59140-032-5

Table of contents :
Data Mining:
Opportunities and
Challenges……Page 2
Copyright……Page 3
Table of Contents……Page 5
Preface……Page 8
References……Page 13
Acknowledgments……Page 14
INTRODUCTION……Page 16
SCHOOLS OF STATISTICS……Page 17
DATA MODEL……Page 21
LOCAL GRAPHICAL MODEL CHOICE……Page 25
GLOBAL GRAPHICAL MODEL CHOICE……Page 30
NON- CATEGORICAL VARIABLES……Page 31
CASE 1: PERSONALIZED MEDIA DISTRIBUTION……Page 35
CASE 2: SCHIZOPHRENIA RESEARCH ¨CCAUSALITY IN COMPLEX SYSTEMS……Page 37
CONCLUSIONS……Page 39
REFERENCES……Page 40
ABSTRACT……Page 42
INTRODUCTION……Page 43
BACKGROUND……Page 46
METHODOLOGIES……Page 55
RESULTS……Page 61
ACKNOWLEDGMENTS……Page 65
REFERENCES……Page 66
ABSTRACT……Page 70
INTRODUCTION……Page 71
COOPERATIVE DATA MINING……Page 72
VISUAL DATA MINING……Page 74
THREE- DIMENSIONAL VISUALIZATION,VIRTUAL REALITY AND DATA MINING……Page 83
CONCLUSIONS……Page 90
REFERENCES……Page 91
ABSTRACT……Page 95
INTRODUCTION……Page 96
EVOLUTIONARY LOCAL SELECTION ALGORITHMS ( ELSA)……Page 97
FEATURE SELECTION IN SUPERVISED LEARNING……Page 99
FEATURE SELECTION IN UNSUPERVISED LEARNING……Page 104
FEATURE SELECTION FOR ENSEMBLES……Page 111
CONCLUSIONS……Page 117
REFERENCES……Page 118
ABSTRACT……Page 121
INTRODUCTION……Page 122
PARALLEL AND DISTRIBUTED DATA MINING……Page 123
STRUCTURED PARALLEL PROGRAMMING……Page 125
STRUCTURED PARALLEL DATA MINING ALGORITHMS……Page 130
ADVANTAGES OF STRUCTURE PARALLELISM……Page 152
CONCLUSIONS……Page 153
REFERENCES……Page 154
INTRODUCTION……Page 157
ROUGH SET THEORY……Page 162
VARIABLE PRECISION ROUGH SET MODEL ( VPRSM)……Page 168
DATA MINING SYSTEM LERS……Page 175
APPLICATIONS……Page 183
CONCLUSIONS……Page 184
REFERENCES……Page 185
THE IMPACT OF MISSING DATA……Page 189
DATA MINING WITH INCONSISTENT DATA/ MISSING DATA……Page 193
METHODS OF ADDRESSING MISSING DATA……Page 196
THE IMPACT OF MISSING DATA ON DATA- MINING ALGORITHMS……Page 202
FUTURE TRENDS……Page 206
CONCLUSIONS……Page 208
REFERENCES……Page 209
ABSTRACT……Page 214
INTRODUCTION……Page 215
RELATED WORK……Page 216
GENERATING CLUSTERS……Page 218
DEVELOPING THEMATIC HIERARCHIES FOR TEXT DOCUMENTS……Page 222
EXPERIMENTAL RESULTS……Page 227
FUTURE WORK……Page 231
REFERENCES……Page 232
INTRODUCTION……Page 235
ORGANIZATIONAL ISSUES……Page 236
STATISTICAL ISSUES……Page 240
DATA ACCURACY AND STANDARDIZATION……Page 243
TECHNICAL ISSUES……Page 247
REFERENCES……Page 251
ABSTRACT……Page 254
INTRODUCTION……Page 255
MOTIVATION……Page 256
BASIC COST MODELS AND ESTIMATION MODELS……Page 258
APPLICATION TO THE DATA……Page 259
ESTIMATION CRITERION QUALITY ISSUES……Page 264
LIMITATIONS……Page 267
EXTENSIONS TO OTHER BASIC COST MODELS……Page 268
DATA- MINING APPLICATIONS AND CONSIDERATIONS……Page 270
CONCLUSIONS……Page 271
REFERENCES……Page 272
APPENDIX……Page 273
ABSTRACT……Page 275
DATA MINING, CLASSIFICATION AND SUPERVISED LEARNING……Page 276
THE BAYESIAN APPROACH TO PROBABILITY……Page 277
BAYESIAN CLASSIFICATION……Page 281
BAYESIAN BELIEF NETWORKS……Page 284
MARKOV CHAIN MONTE CARLO TECHNIQUES……Page 289
CONCLUDING REMARKS……Page 290
REFERENCES……Page 291
INTRODUCTION……Page 293
RELATED WORK……Page 295
MINING NEWSGROUPS¡¯ EXPERTISE……Page 298
MINING FAQS FOR STRUCTURE……Page 300
EVALUATION……Page 307
FUTURE TRENDS……Page 308
CONCLUSION……Page 310
ENDNOTES……Page 311
REFERENCES……Page 312
APPENDIX……Page 314
INTRODUCTION……Page 316
BACKGROUND……Page 318
QUERY- BY- STRUCTURE SYSTEMS……Page 323
FUTURE TRENDS……Page 335
CONCLUSION……Page 336
REFERENCES……Page 337
ABSTRACT……Page 338
INTRODUCTION……Page 339
METHODOLOGY……Page 341
COMPANIES INCLUDED……Page 348
CONSTRUCTING THE MAPS……Page 349
RESULTS……Page 353
CONCLUSIONS……Page 359
FUTURE RESEARCH IN THIS AREA……Page 360
REFERENCES……Page 361
APPENDIX:THE FEATURE PLANES OF THE FINAL MAP……Page 363
INTRODUCTION……Page 365
DATA MINING IN HEALTH CARE……Page 366
COMMUNITY HEALTH INFORMATION NETWORKS ( CHINS)……Page 367
FINDINGS USING A CASE METHODOLOGY……Page 370
HOW THE PLATFORM CAN BE USED……Page 374
DATA- MINING IMPLICATIONS:CONTROVERSIES AND ISSUES ASSOCIATED WITH CHINS……Page 376
REFERENCES……Page 378
INTRODUCTION……Page 381
BACKGROUND……Page 382
APPLYING DATA MINING TECHNIQUES TO HR INFORMATION SYSTEMS……Page 387
PRACTICAL APPLICATIONS OF DATA MINING HR INFORMATION……Page 393
CONCLUSIONS……Page 394
REFERENCES……Page 395
ABSTRACT……Page 397
INTRODUCTION……Page 398
DATA ENVELOPMENT ANALYSIS……Page 399
THE MODEL……Page 403
APPLICATION……Page 405
REFERENCES……Page 409
INTRODUCTION……Page 410
WHAT CAN BE INFERRED FROM DATA?……Page 412
ETHICAL ISSUES……Page 413
SOCIAL IMPLICATIONS……Page 416
LEGAL ISSUES……Page 423
FUTURE TRENDS……Page 431
CONCLUSION……Page 432
REFERENCES……Page 433
ABSTRACT……Page 436
INTRODUCTION……Page 437
USE OF DATA MINING IN DESIGNING THE SYSTEM KNOWLEDGE BASE……Page 438
USE OF DATA MINING IN DESIGNING THE LEARNING PROCESS……Page 441
MULTIAGENT- BASED ARCHITECTURE FOR THE DYNAMIC DSS……Page 448
CONCLUSIONS……Page 450
REFERENCES……Page 451
MAJOR TRENDS IN TECHNOLOGIES AND METHODS: WEB MINING……Page 452
TEXT DATA MINING ( TDM)……Page 455
UBIQUITOUS DM ( UDM)……Page 456
VISUAL DM……Page 457
SPATIAL AND GEOGRAPHIC DM……Page 458
DM TRENDS: METHODS AND TECHNIQUES……Page 459
DM FOR BIOINFORMATICS……Page 460
SUMMARY……Page 461
REFERENCES……Page 462
About the Authors……Page 468
Index……Page 477