Data mining: foundations and intelligent paradigms 1 Clustering, association and classification
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| Other Authors: | |
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| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2012
|
| Series: | Intelligent systems reference library
23 |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XIV, 331 S. graph. Darst. 25 cm |
| ISBN: | 3642231659 9783642231650 |
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| 245 | 1 | 0 | |a Data mining |b foundations and intelligent paradigms |n 1 |p Clustering, association and classification |c Dawn E. Holmes and Lakhmi C. Jain (eds.) |
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IMAGE 1
CONTENTS
CHAPTER 1 DATA MINING TECHNIQUES IN CLUSTERING, ASSOCIATION AND
CLASSIFICATION 1
DAWN E. HOLMES, JEFFREY TWEEDALE, LAKHMI C. JAIN 1 INTRODUCTION 1
1.1 DATA 1
1.2 KNOWLEDGE 2
1.3 CLUSTERING 2
1.4 ASSOCIATION 3
1.5 CLASSIFICATION 3
2 DATA MINING 4
2.1 METHODS AND ALGORITHMS 4
2.2 APPLICATIONS 4
3 CHAPTERS INCLUDED IN THE BOOK 5
4 CONCLUSION 5
REFERENCES 6
CHAPTER 2
CLUSTERING ANALYSIS IN LARGE GRAPHS WITH RICH ATTRIBUTES 7 YANG ZHOU,
LING LIU 1 INTRODUCTION 8
2 GENERAL ISSUES IN GRAPH CLUSTERING 11
2.1 GRAPH PARTITION TECHNIQUES 12
2.2 BASIC PREPARATION FOR GRAPH CLUSTERING 14
2.3 GRAPH CLUSTERING WITH SA-CLUSTER 15
3 GRAPH CLUSTERING BASED ON STRUCTURAL/ATTRIBUTE SIMILARITIES . . 16 4
THE INCREMENTAL ALGORITHM 19
5 OPTIMIZATION TECHNIQUES 21
5.1 THE STORAGE COST AND OPTIMIZATION 22
5.2 MATRIX COMPUTATION OPTIMIZATION 23
5.3 PARALLELISM 24
6 CONCLUSION 24
REFERENCES 25
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1013510917
DIGITALISIERT DURCH
IMAGE 2
VIII CONTENTS
CHAPTER 3
TEMPORAL DATA MINING: SIMILARITY-PROFILED ASSOCIATION PATTERN 29
JIN SOUNG YOO 1 INTRODUCTION 29
2 SIMILARITY-PROFILED TEMPORAL ASSOCIATION PATTERN 32
2.1 PROBLEM STATEMENT 32
2.2 INTEREST MEASURE 34
3 MINING ALGORITHM 35
3.1 ENVELOPE OF SUPPORT TIME SEQUENCE 35
3.2 LOWER BOUNDING DISTANCE 36
3.3 MONOTONICITY PROPERTY OF UPPER LOWER-BOUNDING DISTANCE 38
3.4 SPAMINE ALGORITHM 39
4 EXPERIMENTAL EVALUATION 41
5 RELATED WORK 43
6 CONCLUSION 45
REFERENCES 45
CHAPTER 4
BAYESIAN NETWORKS WITH IMPRECISE PROBABILITIES: THEORY AND APPLICATION
TO CLASSIFICATION 49
G. CORANI, A. ANTONUCCI, M. ZAFFALON 1 INTRODUCTION 49
2 BAYESIAN NETWORKS 51
3 CREDAL SETS 52
3.1 DEFINITION 53
3.2 BASIC OPERATIONS WITH CREDAL SETS 53
3.3 CREDAL SETS FROM PROBABILITY INTERVALS 55
3.4 LEARNING CREDAL SETS FROM DATA 55
4 CREDAL NETWORKS 56
4.1 CREDAL NETWORK DEFINITION AND STRONG EXTENSION 56 4.2 NON-SEPARATELY
SPECIFIED CREDAL NETWORKS 57
5 COMPUTING WITH CREDAL NETWORKS 60
5.1 CREDAL NETWORKS UPDATING 60
5.2 ALGORITHMS FOR CREDAL NETWORKS UPDATING 61
5.3 MODELLING AND UPDATING WITH MISSING DATA 62
6. AN APPLICATION: ASSESSING ENVIRONMENTAL RISK BY CREDAL NETWORKS 64
6.1 DEBRIS FLOWS 64
6.2 THE CREDAL NETWORK 65
7 CREDAL CLASSIFIERS 70
8 NAIVE BAYES 71
8.1 MATHEMATICAL DERIVATION 73
9 NAIVE CREDAL CLASSIFIER (NCC) 74
IMAGE 3
CONTENTS IX
9.1 COMPARING NBC AND NCC IN TEXTURE RECOGNITION 76 9.2 TREATMENT OF
MISSING DATA 79
10 METRICS FOR CREDAL CLASSIFIERS 80
11 TREE-AUGMENTED NAIVE BAYES (TAN) 81
11.1 VARIANTS OF THE IMPRECISE DIRICHLET MODEL: LOCAL AND GLOBAL IDM 82
12 CREDAL TAN 83
13 FURTHER CREDAL CLASSIFIERS 85
13.1 LAZY NCC (LNCC) 85
13.2 CREDAL MODEL AVERAGING (CMA) 86
14 OPEN SOURCE SOFTWARE 88
15 CONCLUSIONS 88
REFERENCES 88
CHAPTER 5
HIERARCHICAL CLUSTERING FOR FINDING SYMMETRIES AND OTHER PATTERNS IN
MASSIVE, HIGH DIMENSIONAL DATASETS 95
FIONN MURTAGH, PEDRO CONTRERAS 1 INTRODUCTION: HIERARCHY AND OTHER
SYMMETRIES IN DATA ANALYSIS 95
1.1 ABOUT THIS ARTICLE 96
1.2 A BRIEF INTRODUCTION TO HIERARCHICAL CLUSTERING 96
1.3 A BRIEF INTRODUCTION TO P-ADIC NUMBERS 97
1.4 BRIEF DISCUSSION OF P-ADIC AND M-ADIC NUMBERS 98
2 ULTRAMETRIC TOPOLOGY 98
2.1 ULTRAMETRIC SPACE FOR REPRESENTING HIERARCHY 98
2.2 SOME GEOMETRICAL PROPERTIES OF ULTRAMETRIC SPACES 100 2.3
ULTRAMETRIC MATRICES AND THEIR PROPERTIES 100
2.4 CLUSTERING THROUGH MATRIX ROW AND COLUMN PERMUTATION 101
2.5 OTHER MISCELLANEOUS SYMMETRIES 103
3 GENERALIZED ULTRAMETRIC 103
3.1 LINK WITH FORMAL CONCEPT ANALYSIS 103
3.2 APPLICATIONS OF GENERALIZED ULTRAMETRICS 104
3.3 EXAMPLE OF APPLICATION: CHEMICAL DATABASE MATCHING 105
4 HIERARCHY IN A P-ADIC NUMBER SYSTEM 110
4.1 P-ADIC ENCODING OF A DENDROGRAM 110
4.2 P-ADIC DISTANCE ON A DENDROGRAM 113
4.3 SCALE-RELATED SYMMETRY 114
5 TREE SYMMETRIES THROUGH THE WREATH PRODUCT GROUP 114
5.1 WREATH PRODUCT GROUP CORRESPONDING TO A HIERARCHICAL CLUSTERING 115
5.2 WREATH PRODUCT INVARIANCE 115
IMAGE 4
X CONTENTS
5.3 EXAMPLE OF WREATH PRODUCT INVARIANCE: HAAR WAVELET TRANSFORM OF A
DENDROGRAM 116
6 REMARKABLE SYMMETRIES IN VERY HIGH DIMENSIONAL SPACES 118 6.1
APPLICATION TO VERY HIGH FREQUENCY DATA ANALYSIS: SEGMENTING A FINANCIAL
SIGNAL 119
7 CONCLUSIONS 126
REFERENCES 126
CHAPTER 6
RANDOMIZED ALGORITHM OF FINDING THE TRUE NUMBER OF CLUSTERS BASED ON
CHEBYCHEV POLYNOMIAL APPROXIMATION 131 R. AVROS, O. GRANICHIN, D.
SHALYMOV, Z. VOLKOVICH, G.-W. WEBER 1 INTRODUCTION 131
2 CLUSTERING 135
2.1 CLUSTERING METHODS 135
2.2 STABILITY BASED METHODS 138
2.3 GEOMETRICAL CLUSTER VALIDATION CRITERIA 141
3 RANDOMIZED ALGORITHM 144
4 EXAMPLES 147
5 CONCLUSION 152
REFERENCES 152
CHAPTER 7
BREGMAN BUBBLE CLUSTERING: A ROBUST FRAMEWORK FOR MINING DENSE CLUSTERS
157
JOYDEEP GHOSH, GUNJAN GUPTA 1 INTRODUCTION 157
2 BACKGROUND 161
2.1 PARTITIONAL CLUSTERING USING BREGMAN DIVERGENCES 161 2.2
DENSITY-BASED AND MODE SEEKING APPROACHES TO CLUSTERING 162
2.3 ITERATIVE RELOCATION ALGORITHMS FOR FINDING A SINGLE DENSE REGION
164
2.4 CLUSTERING A SUBSET OF DATA INTO MULTIPLE OVERLAPPING CLUSTERS 165
3 BREGMAN BUBBLE CLUSTERING 165
3.1 COST FUNCTION 165
3.2 PROBLEM DEFINITION 166
3.3 BREGMANIAN BALLS AND BREGMAN BUBBLES 166
3.4 BBC-S: BREGMAN BUBBLE CLUSTERING WITH FIXED CLUSTERING SIZE 167
3.5 BBC-Q: DUAL FORMULATION OF BREGMAN BUBBLE CLUSTERING WITH FIXED COST
169
IMAGE 5
CONTENTS XI
4 SOFT BREGMAN BUBBLE CLUSTERING (SOFT BBC) 169
4.1 BREGMAN SOFT CLUSTERING 169
4.2 MOTIVATIONS FOR DEVELOPING SOFT BBC 170
4.3 GENERATIVE MODEL 171
4.4 SOFT BBC EM ALGORITHM 171
4.5 CHOOSING AN APPROPRIATE PO 173
5 IMPROVING LOCAL SEARCH: PRESSURIZATION 174
5.1 BREGMAN BUBBLE PRESSURE 174
5.2 MOTIVATION 175
5.3 BBC-PRESS 176
5.4 SOFT BBC-PRESS 177
5.5 PRESSURIZATION VS. DETERMINISTIC ANNEALING 177
6 A UNIFIED FRAMEWORK 177
6.1 UNIFYING SOFT BREGMAN BUBBLE AND BREGMAN BUBBLE CLUSTERING 177
6.2 OTHER UNIFICATIONS 178
7 EXAMPLE: BREGMAN BUBBLE CLUSTERING WITH GAUSSIANS 180 7.1 A 2 IS FIXED
180
7.2 A 2 IS OPTIMIZED 181
7.3 "FLAVORS" OF BBC FOR GAUSSIANS 182
7.4 MIXTURE-6: AN ALTERNATIVE TO BBC USING A GAUSSIAN BACKGROUND 182
8 EXTENDING BBOCC & BBC TO PEARSON DISTANCE AND COSINE SIMILARITY 183
8.1 PEARSON CORRELATION AND PEARSON DISTANCE 183
8.2 EXTENSION TO COSINE SIMILARITY 185
8.3 PEARSON DISTANCE VS. (1-COSINE SIMILARITY) VS. OTHER BREGMAN
DIVERGENCES - WHICH ONE TO USE WHERE? 185 9 SEEDING BBC AND DETERMINING
K USING DENSITY GRADIENT ENUMERATION (DGRADE) 185
9.1 BACKGROUND 186
9.2 DGRADE ALGORITHM 186
9.3 SELECTING S ONE : THE SMOOTHING PARAMETER FOR DGRADE 188
10 EXPERIMENTS 190
10.1 OVERVIEW 190
10.2 DATASETS 190
10.3 EVALUATION METHODOLOGY 192
10.4 RESULTS FOR BBC WITH PRESSURIZATION 194
10.5 RESULTS ON BBC WITH DGRADE 198
11 CONCLUDING REMARKS 202
REFERENCES 204
IMAGE 6
XII CONTENTS
CHAPTER 8
DEPMINER: A METHOD AND A SYSTEM FOR THE EXTRACTION OF SIGNIFICANT
DEPENDENCIES 209
ROSA MEO, LEONARDO D'AMBROSI 1 INTRODUCTION 209
2 RELATED WORK 211
3 ESTIMATION OF THE REFERENTIAL PROBABILITY 213
4 SETTING A THRESHOLD FOR A 213
5 EMBEDDING A N IN ALGORITHMS 215
6 DETERMINATION OF THE ITEMSETS MINIMUM SUPPORT THRESHOLD . . 216 7
SYSTEM DESCRIPTION 218
8 EXPERIMENTAL EVALUATION 220
9 CONCLUSIONS 221
REFERENCES 221
CHAPTER 9
INTEGRATION OF DATASET SCANS IN PROCESSING SETS OF FREQUENT ITEMSET
QUERIES 223
MAREK WOJCIECHOWSKI, MACIEJ ZAKRZEWICZ, PAWEL BOINSKI 1 INTRODUCTION 223
2 FREQUENT ITEMSET MINING AND APRIORI ALGORITHM 225
2.1 BASIC DEFINITIONS AND PROBLEM STATEMENT 225
2.2 ALGORITHM APRIORI 226
3 FREQUENT ITEMSET QUERIES - STATE OF THE ART 227
3.1 FREQUENT ITEMSET QUERIES 227
3.2 CONSTRAINT-BASED FREQUENT ITEMSET MINING 229
3.3 REUSING RESULTS OF PREVIOUS FREQUENT ITEMSET QUERIES. 230 4
OPTIMIZING SETS OF FREQUENT ITEMSET QUERIES 231
4.1 BASIC DEFINITIONS 232
4.2 PROBLEM FORMULATION 233
4.3 RELATED WORK ON MULTI-QUERY OPTIMIZATION 234 5 COMMON COUNTING 234
5.1 BASIC ALGORITHM 234
5.2 MOTIVATION FOR QUERY SET PARTITIONING 237
5.3 KEY ISSUES REGARDING QUERY SET PARTITIONING 237 6 FREQUENT ITEMSET
QUERY SET PARTITIONING BY HYPERGRAPH PARTITIONING 238
6.1 DATA SHARING HYPERGRAPH 239
6.2 HYPERGRAPH PARTITIONING PROBLEM FORMULATION 239 6.3 COMPUTATION
COMPLEXITY OF THE PROBLEM 241
6.4 RELATED WORK ON HYPERGRAPH PARTITIONING 241
7 QUERY SET PARTITIONING ALGORITHMS 241
7.1 CCRECURSIVE 242
7.2 CCFULL 243
7.3 CCCOARSENING 246
IMAGE 7
CONTENTS XIII
7.4 CCAGGLOMERATIVE 247
7.5 CCAGGLOMERATIVENOISE 248
7.6 CCGREEDY 249
7.7 CCSEMIGREEDY 250
8 EXPERIMENTAL RESULTS 251
8.1 COMPARISON OF BASIC DEDICATED ALGORITHMS 252
8.2 COMPARISON OF GREEDY APPROACHES WITH THE BEST DEDICATED ALGORITHMS
257
9 REVIEW OF OTHER METHODS OF PROCESSING SETS OF FREQUENT ITEMSET QUERIES
260
10 CONCLUSIONS 261
REFERENCES 262
CHAPTER 10
TEXT CLUSTERING WITH NAMED ENTITIES: A MODEL, EXPERIMENTATION AND
REALIZATION 267
TRU H. CAO, THAO M. TANG, CUONG K. CHAU 1 INTRODUCTION 267
2 AN ENTITY-KEYWORD MULTI-VECTOR SPACE MODEL 269
3 MEASURES OF CLUSTERING QUALITY 271
4 HARD CLUSTERING EXPERIMENTS 273
5 FUZZY CLUSTERING EXPERIMENTS 277
6 TEXT CLUSTERING IN VN-KIM SEARCH 282
7 CONCLUSION 285
REFERENCES 286
CHAPTER 11
REGIONAL ASSOCIATION RULE MINING AND SCOPING FROM SPATIAL DATA 289
WEI DING, CHRISTOPH F. EICK 1 INTRODUCTION 289
2 RELATED WORK 291
2.1 HOT-SPOT DISCOVERY 291
2.2 SPATIAL ASSOCIATION RULE MINING 292
3 THE FRAMEWORK FOR REGIONAL ASSOCIATION RULE MINING AND SCOPING 293
3.1 REGION DISCOVERY 293
3.2 PROBLEM FORMULATION 294
3.3 MEASURE OF INTERESTINGNESS 295
4 ALGORITHMS 298
4.1 REGION DISCOVERY 298
4.2 GENERATION OF REGIONAL ASSOCIATION RULES 301
IMAGE 8
XIV CONTENTS
5 ARSENIC REGIONAL ASSOCIATION RULE MINING AND SCOPING IN THE TEXAS
WATER SUPPLY 302
5.1 DATA COLLECTION AND DATA PREPROCESSING 302
5.2 REGION DISCOVERY FOR ARSENIC HOT/COLD SPOTS 304
5.3 REGIONAL ASSOCIATION RULE MINING 305
5.4 REGION DISCOVERY FOR REGIONAL ASSOCIATION RULE SCOPING 307
6 SUMMARY 310
REFERENCES 311
CHAPTER 12
LEARNING FROM IMBALANCED DATA: EVALUATION MATTERS 315
TROY RAEDER, GEORGE FORMAN, NITESH V. CHAWLA 1 MOTIVATION AND
SIGNIFICANCE 315
2 PRIOR WORK AND LIMITATIONS 317
3 EXPERIMENTS 318
3.1 DATASETS 321
3.2 EMPIRICAL ANALYSIS 321
4 DISCUSSION AND RECOMMENDATIONS 325
4.1 COMPARISONS OF CLASSIFIERS 325
4.2 TOWARDS PARTS-PER-MILLION 328
4.3 RECOMMENDATIONS 329
5 SUMMARY 329
REFERENCES 330
AUTHOR INDEX 333 |
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| indexdate | 2024-07-21T00:31:11Z |
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| spelling | Data mining foundations and intelligent paradigms 1 Clustering, association and classification Dawn E. Holmes and Lakhmi C. Jain (eds.) Berlin ; Heidelberg Springer 2012 XIV, 331 S. graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Intelligent systems reference library 23 Intelligent systems reference library ... Holmes, Dawn E. edt (DE-604)BV040111717 1 Erscheint auch als Online-Ausgabe 978-3-642-23166-7 Intelligent systems reference library 23 (DE-604)BV035704685 23 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3853504&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024968034&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Data mining foundations and intelligent paradigms Intelligent systems reference library |
| title | Data mining foundations and intelligent paradigms |
| title_auth | Data mining foundations and intelligent paradigms |
| title_exact_search | Data mining foundations and intelligent paradigms |
| title_full | Data mining foundations and intelligent paradigms 1 Clustering, association and classification Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_fullStr | Data mining foundations and intelligent paradigms 1 Clustering, association and classification Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_full_unstemmed | Data mining foundations and intelligent paradigms 1 Clustering, association and classification Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_short | Data mining |
| title_sort | data mining foundations and intelligent paradigms clustering association and classification |
| title_sub | foundations and intelligent paradigms |
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| volume_link | (DE-604)BV040111717 (DE-604)BV035704685 |
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