Data mining: foundations and intelligent paradigms 3 Medical, health, social, biological and other applications
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| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2012
|
| Series: | Intelligent systems reference library
25 |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XIII, 361 S. Ill., graph. Darst. 25 cm |
| ISBN: | 3642231500 9783642231506 |
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| 245 | 1 | 0 | |a Data mining |b foundations and intelligent paradigms |n 3 |p Medical, health, social, biological and other applications |c Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| 264 | 1 | |a Berlin ; Heidelberg |b Springer |c 2012 | |
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Record in the Search Index
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IMAGE 1
CONTENTS
CHAPTER 1 ADVANCES IN INTELLIGENT DATA MINING 1
DAWN E. HOLMES, JEFFREY W. TWEEDALE, LAKHMI C. JAIN 1 INTRODUCTION 1
2 MEDICAL INFLUENCES 2
3 HEALTH INFLUENCES 2
4 SOCIAL INFLUENCES 2
4.1 INFORMATION DISCOVERY 2
4.2 ON-LINE COMMUNITIES 3
5 BIOLOGICAL INFLUENCES 3
5.1 BIOLOGICAL NETWORKS 3
5.2 ESTIMATIONS IN GENE EXPRESSION 4
6 CHAPTERS INCLUDED IN THE BOOK 4
7 CONCLUSION 6
REFERENCES 6
CHAPTER 2 TEMPORAL PATTERN MINING FOR MEDICAL APPLICATIONS 9
GIULIA BRUNO, PAOLO GARZA 1 INTRODUCTION 9
2 TYPES OF TEMPORAL DATA IN MEDICAL DOMAIN 10
3 DEFINITIONS 11
4 TEMPORAL PATTERN MINING ALGORITHMS 11
4.1 TEMPORAL PATTERN MINING FROM A SET OF SEQUENCES 12 4.2 TEMPORAL
PATTERN MINING FROM A SINGLE SEQUENCE 14 5 MEDICAL APPLICATIONS 15
6 CONCLUSIONS 17
REFERENCES 18
CHAPTER 3 BIOKEYSPOTTER: AN UNSUPERVISED KEYPHRASE EXTRACTION TECHNIQUE
IN THE BIOMEDICAI FULL-TEXT COLLECTION 19
MIN SONG, PRAT TANAPAISANKIT 1 INTRODUCTION 19
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1013510585
DIGITALISIERT DURCH
IMAGE 2
VILI CONTENTS
2 BACKGROUNDS AND RELATED WORK 20
3 THE PROPOSED APPROACH 21
4 EVALUATION 23
4.1 DATASET 24
4.2 COMPARISON ALGORITHMS 24
4.3 EXPERIMENTAL RESULTS 25
5 CONCLUSION 26
REFERENCES 27
CHAPTER 4
MINING HEALTH CLAIMS DATA FOR ASSESSING PATIENT RISK 29
IAN DUNCAN 1 WHAT IS HEALTH RISK? 29
2 TRADITIONAL MODELS FOR ASSESSING HEALTH RISK 33
3 RISK FACTOR-BASED RISK MODELS 37
4 DATA SOURCES 39
4.1 ENROLLMENT DATA 40
4.2 CLAIMS AND CODING SYSTEMS 40
4.3 INTERPRETATION OF CLAIMS CODES 49
5 CLINICAL IDENTIFICATION ALGORITHMS 51
6 SENSITIVITY-SPECIFICITY TRADE-OFF 56
6.1 CONSTRUCTING AN IDENTIFICATION ALGORITHM 56
6.2 SOURCES OF ALGORITHMS 57
7 CONSTRUCTION AND USE OF GROUPER MODELS 58
7.1 DRUG GROUPER MODELS 61
7.2 DRUG-BASED RISK ADJUSTMENT MODELS 61
8 SUMMARY AND CONCLUSIONS 62
REFERENCES 62
CHAPTER 5
MINING BIOLOGICAL NETWORKS FOR SIMILAR PATTERNS 63
FERHAT AY, GIINHAN GIILSOY, TAMER KAHVECI 1 INTRODUCTION 63
2 METABOLIC NETWORK ALIGNMENT WITH ONE-TO-ONE MAPPINGS 67 2.1 MODEL 68
2.2 PROBLEM FORMULATION 69
2.3 PAIRWISE SIMILARITY OF ENTITIES 70
2.4 SIMILARITY OF TOPOLOGIES 74
2.5 COMBINING HOMOLOGY AND TOPOLOGY 76
2.6 EXTRACTING THE MAPPING OF ENTITIES 78
2.7 SIMILARITY SCORE OF NETWORKS 79
2.8 COMPLEXITY ANALYSIS 80
3 METABOLIC NETWORK ALIGNMENT WITH ONE-TO-MANY MAPPINGS . . 80 3.1
HOMOLOGICAL SIMILARITY OF SUBNETWORKS 82
3.2 TOPOLOGICAL SIMILARITY OF SUBNETWORKS 83
IMAGE 3
CONTENTS IX
3.3 COMBINING HOMOLOGY AND TOPOLOGY 84
3.4 EXTRACTING SUBNETWORK MAPPINGS 84
4 SIGNIFICANCE OF NETWORK ALIGNMENT 88
4.1 IDENTIFICATION OF ALTERNATIVE ENTITIES 88
4.2 IDENTIFICATION OF ALTERNATIVE SUBNETWORKS 89
4.3 ONE-TO-MANY MAPPINGS WITHIN AND ACROSS MAJOR CLADES 91
5 SUMMARY 92
6 FURTHER READING 93
REFERENCES 96
CHAPTER 6
ESTIMATION OF DISTRIBUTION ALGORITHMS IN GENE EXPRESSION DATA ANALYSIS
101
ELHAM SALEHI, ROBIN GRAS 1 INTRODUCTION 101
2 ESTIMATION OF DISTRIBUTION OF ALGORITHMS 102
2.1 MODEL BUILDING IN EDA 103
2.2 NOTATION 104
2.3 MODELS WITH INDEPENDENT VARIABLES 104
2.4 MODELS WITH PAIR WISE DEPENDENCIES 105
2.5 MODELS WITH MULTIPLE DEPENDENCIES 106
3 APPLICATION OF EDA IN GENE EXPRESSION DATA ANALYSIS 108 3.1
STATE-OF-ART OF THE APPLICATION OF ED AS IN GENE EXPRESSION DATA
ANALYSIS 110
4 CONCLUSION 116
REFERENCES 116
CHAPTER 7
GENE FUNCTION PREDICTION AND FUNCTIONAL NETWORK: THE ROLE OF GENE
ONTOLOGY 123
ERLIANG ZENG, CHRIS DING, KALAI MATHEE, LISA SCHNEPER, GIRI NARASIMHAN 1
INTRODUCTION 124
1.1 GENE FUNCTION PREDICTION 125
1.2 FUNCTIONAL GENE NETWORK GENERATION 127
1.3 RELATED WORK AND LIMITATIONS 128
2 GO-BASED GENE SIMILARITY MEASURES 129
3 ESTIMATING SUPPORT FOR PPI DATA WITH APPLICATIONS TO FUNCTION
PREDICTION 132
3.1 MIXTURE MODEL OF PPI DATA 132
3.2 DATA SETS 133
3.3 FUNCTION PREDICTION 134
3.4 EVALUATING THE FUNCTION PREDICTION 135
3.5 EXPERIMENTAL RESULTS 137
3.6 DISCUSSION 147
IMAGE 4
X CONTENTS
4 A FUNCTIONAL NETWORK OF YEAST GENES USING GENE ONTOLOGY INFORMATION
149
4.1 DATA SETS 149
4.2 CONSTRUCTING A FUNCTIONAL GENE NETWORK 149
4.3 USING SEMANTIC SIMILARITY (SS) 150
4.4 EVALUATING THE FUNCTIONAL GENE NETWORK 151
4.5 EXPERIMENTAL RESULTS 151
4.6 DISCUSSION 158
5 CONCLUSIONS 159
REFERENCES 160
CHAPTER 8
MINING MULTIPLE BIOLOGICAL DATA FOR RECONSTRUCTING SIGNAL TRANSDUCTION
NETWORKS 163
THANH-PHUONG NGUYEN, TU-BAO HO 1 INTRODUCTION 163
2 BACKGROUND 164
2.1 SIGNAL TRANSDUCTION NETWORK 164
2.2 PROTEIN-PROTEIN INTERACTION 166
3 CONSTRUCTING SIGNAL TRANSDUCTION NETWORKS USING MULTIPLE DATA 167
3.1 RELATED WORK 167
3.2 MATERIALS AND METHODS 168
3.3 CLUSTERING AND PROTEIN-PROTEIN INTERACTION NETWORKS. 169 3.4
EVALUATION 174
4 SOME RESULTS OF YEAST STN RECONSTRUCTION 178
5 OUTLOOK 180
6 SUMMARY 181
REFERENCES 181
CHAPTER 9
MINING EPISTATIC INTERACTIONS FROM HIGH-DIMENSIONAL DATA SETS 187
XIA JIANG, SHYAM VISWESWARAN, RICHARD E. NEAPOLITAN 1 INTRODUCTION 187
2 BACKGROUND 188
2.1 EPISTASIS 188
2.2 DETECTING EPISTASIS 189
2.3 HIGH-DIMENSIONAL DATA SETS 190
2.4 BARRIERS TO LEARNING EPISTASIS 191
2.5 MDR 191
2.6 BAYESIAN NETWORKS 193
3 DISCOVERING EPISTASIS USING BAYESIAN NETWORKS 196
3.1 A BAYESIAN NETWORK MODEL FOR EPISTATIC INTERACTIONS . . 196 3.2 THE
BNMBL SCORE 197
IMAGE 5
CONTENTS XI
3.3 EXPERIMENTS 197
4 EFFICIENT SEARCH 202
4.1 EXPERIMENTS 203
5 DISCUSSION, LIMITATIONS, AND FUTURE RESEARCH 206
REFERENCES 207
CHAPTER 10
KNOWLEDGE DISCOVERY IN ADVERSARIAL SETTINGS 211
D.B. SKILLICORN 1 INTRODUCTION 211
2 CHARACTERISTICS OF ADVERSARIAL MODELLING 214
3 TECHNICAL IMPLICATIONS 216
4 CONCLUSION 221
REFERENCES 222
CHAPTER 11
ANALYSIS AND MINING OF ONLINE COMMUNITIES OF INTERNET FORUM USERS 225
MIKOLAJ MORZY 1 INTRODUCTION 225
1.1 WHAT IS WEB 2.0? 225
1.2 NEW FORMS OF PARTICIPATION - PUSH OR PULL? 228
1.3 INTERNET FORUMS AS NEW FORMS OF CONVERSATION 229
2 SOCIAL-DRIVEN DATA 231
2.1 WHAT ARE SOCIAL-DRIVEN DATA? 231
2.2 DATA FROM INTERNET FORUMS 234
3 INTERNET FORUMS 237
3.1 CRAWLING INTERNET FORUMS 237
3.2 STATISTICAL ANALYSIS 239
3.3 INDEX ANALYSIS 246
3.4 NETWORK ANALYSIS 253
4 RELATED WORK 260
5 CONCLUSIONS 261
REFERENCES 262
CHAPTER 12
DATA MINING FOR INFORMATION LITERACY 265
BETTINA BERENDT 1 INTRODUCTION 265
2 BACKGROUND 267
2.1 INFORMATION LITERACY 267
2.2 CRITICAL LITERACY 269
2.3 EDUCATIONAL DATA MINING 270
3 TOWARDS CRITICAL DATA LITERACY: A FRAME FOR ANALYSIS AND DESIGN 270
IMAGE 6
XII CONTENTS
3.1 A FRAME OF ANALYSIS: TECHNIQUE AND OBJECT 270
3.2 ON THE CHANCES OF ACHIEVING CRITICAL DATA LITERACY: PRINCIPLES OF
SUCCESSFUL LEARNING AS DESCRIPTION CRITERIA 272
4 EXAMPLES: TOOLS AND OTHER APPROACHES SUPPORTING DATA MINING FOR
INFORMATION LITERACY 273
4.1 ANALYSING DATA: DO-IT-YOURSELF STATISTICS VISUALIZATION 273
4.2 ANALYSING LANGUAGE: VIEWPOINTS AND BIAS IN MEDIA REPORTING 277
4.3 ANALYSING DATA MINING: BUILDING, COMPARING AND RE-USING OWN AND
OTHERS' CONCEPTUALIZATIONS OF A DOMAIN 282
4.4 ANALYSING ACTIONS: FEEDBACK AND AWARENESS TOOLS 284 4.5 ANALYSING
ACTIONS: ROLE REVERSALS IN DATA COLLECTION AND ANALYSIS 288
5 SUMMARY AND CONCLUSIONS 292
REFERENCES 293
CHAPTER 13
RULE EXTRACTION FROM NEURAL NETWORKS AND SUPPORT VECTOR MACHINES FOR
CREDIT SCORING 299
RUDY SETIONO, BART BAESENS, DAVID MARTENS 1 INTRODUCTION 299
2 RE-RX: RECURSIVE RULE EXTRACTION FROM NEURAL NETWORKS 300 2.1
MULTILAYER PERCEPTRON 300
2.2 FINDING OPTIMAL NETWORK STRUCTURE BY PRUNING 303 2.3 RECURSIVE RULE
EXTRACTION 304
2.4 APPLYING RE-RX FOR CREDIT SCORING 306
3 ALBA: RULE EXTRACTION FROM SUPPORT VECTOR MACHINES 311 3.1 SUPPORT
VECTOR MACHINE 311
3.2 ALBA: ACTIVE LEARNING BASED APPROACH TO SVM RULE EXTRACTION 313
3.3 APPLYING ALBA FOR CREDIT SCORING 316
4 CONCLUSION 318
REFERENCES 318
CHAPTER 14
USING SELF-ORGANIZING MAP FOR DATA MINING: A SYNTHESIS WITH ACCOUNTING
APPLICATIONS 321
ANDRIY ANDREEV, ARGYRIS ARGYROU 1 INTRODUCTION 321
2 DATA PRE-PROCESSING 322
2.1 TYPES OF VARIABLES 322
2.2 DISTANCE METRICS 323
IMAGE 7
CONTENTS XIII
2.3 RESCALING INPUT VARIABLES 323
3 SELF-ORGANIZING MAP 324
3.1 INTRODUCTION TO SOM 324
3.2 FORMATION OF SOM 324
4 PERFORMANCE METRICS AND CLUSTER VALIDITY 326
5 EXTENSIONS OF SOM 328
5.1 NON-METRIC SPACES 328
5.2 SOM FOR TEMPORAL SEQUENCE PROCESSING 329
5.3 SOM FOR CLUSTER ANALYSIS 331
5.4 SOM FOR VISUALIZING HIGH-DIMENSIONAL DATA 333
6 FINANCIAL APPLICATIONS OF SOM 334
7 CASE STUDY: CLUSTERING ACCOUNTING DATABASES 335
7.1 DATA DESCRIPTION 335
7.2 DATA PRE-PROCESSING 336
7.3 EXPERIMENTS 337
7.4 RESULTS PRESENTATION AND DISCUSSION 338
REFERENCES 338
CHAPTER 15
APPLYING DATA MINING TECHNIQUES TO ASSESS STEEL PLANT OPERATION
CONDITIONS '. 343
KHAN MUHAMMAD BADRUDDIN, ISAO YAGI, TAKAO TERANO 1 INTRODUCTION 343
2 BRIEF DESCRIPTION OF EAF 345
2.1 PERFORMANCE EVALUATION CRITERIA 346
2.2 INNOVATIONS IN ELECTRIC ARC FURNACES 346
2.3 DETAILS OF THE OPERATION 347
2.4 UNDERSTANDING SCIPS AND STAGES OF A HEAT 349
3 PROBLEM DESCRIPTION 350
4 DATA MINING PROCESS 351
4.1 DATA 351
4.2 DATA PREPROCESSING 351
4.3 ATTRIBUTE PRUNING 353
4.4 THE EXPERIMENTS 354
4.5 DATA MINING TECHNIQUES 354
5 RESULTS 355
5.1 DISCUSSION 358
6 CONCLUDING REMARKS 359
REFERENCES 360
AUTHOR INDEX 363 |
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| spelling | Data mining foundations and intelligent paradigms 3 Medical, health, social, biological and other applications Dawn E. Holmes and Lakhmi C. Jain (eds.) Berlin ; Heidelberg Springer 2012 XIII, 361 S. Ill., graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Intelligent systems reference library 25 Intelligent systems reference library ... Holmes, Dawn E. edt (DE-604)BV040111717 3 Erscheint auch als Online-Ausgabe 978-3-642-23151-3 Intelligent systems reference library 25 (DE-604)BV035704685 25 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3853471&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=024968039&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 3 Medical, health, social, biological and other applications Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_fullStr | Data mining foundations and intelligent paradigms 3 Medical, health, social, biological and other applications Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_full_unstemmed | Data mining foundations and intelligent paradigms 3 Medical, health, social, biological and other applications Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_short | Data mining |
| title_sort | data mining foundations and intelligent paradigms medical health social biological and other applications |
| title_sub | foundations and intelligent paradigms |
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