Data mining: foundations and intelligent paradigms 2 Statistical, Bayesian, time series and other theoretical aspects
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin ; Heidelberg
Springer
2012
|
| Schriftenreihe: | Intelligent systems reference library
24 |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XII, 246 S. graph. Darst. 25 cm |
| ISBN: | 364223240X 9783642232404 |
Internformat
MARC
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| 245 | 1 | 0 | |a Data mining |b foundations and intelligent paradigms |n 2 |p Statistical, Bayesian, time series and other theoretical aspects |c Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| 264 | 1 | |a Berlin ; Heidelberg |b Springer |c 2012 | |
| 300 | |a XII, 246 S. |b graph. Darst. |c 25 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Intelligent systems reference library |v 24 | |
| 490 | 0 | |a Intelligent systems reference library |v ... | |
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Datensatz im Suchindex
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IMAGE 1
CONTENTS
CHAPTER 1 ADVANCED MODELLING PARADIGMS IN DATA MINING 1
DAWN E. HOLMES, JEFFREY TWEEDALE, LAKHMI C. JAIN 1 INTRODUCTION 1
2 FOUNDATIONS 1
2.1 STATISTICAL MODELLING 2
2.2 PREDICTIONS ANALYSIS 2
2.3 DATA ANALYSIS 3
2.4 CHAINS OF RELATIONSHIPS 3
3 INTELLIGENT PARADIGMS 4
3.1 BAYESIAN ANALYSIS 4
3.2 SUPPORT VECTOR MACHINES 4
3.3 LEARNING 5
4 CHAPTERS INCLUDED IN THE BOOK 5
5 CONCLUSION 6
REFERENCES 7
CHAPTER 2
DATA MINING WITH MULTILAYER PERCEPTRONS AND SUPPORT VECTOR MACHINES 9
PAULO CORTEZ 1 INTRODUCTION 9
2 SUPERVISED LEARNING 10
2.1 CLASSICAL REGRESSION 11
2.2 MULTILAYER PERCEPTRON 11
2.3 SUPPORT VECTOR MACHINES 13
3 DATA MINING 14
3.1 BUSINESS UNDERSTANDING 14
3.2 DATA UNDERSTANDING 14
3.3 DATA PREPARATION 15
3.4 MODELING 15
3.5 EVALUATION 18
3.6 DEPLOYMENT 18
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1013510593
DIGITALISIERT DURCH
IMAGE 2
VIII CONTENTS
4 EXPERIMENTS 19
4.1 CLASSIFICATION EXAMPLE 19
4.2 REGRESSION EXAMPLE 21
5 CONCLUSIONS AND FURTHER READING 23
REFERENCES 23
CHAPTER 3
REGULATORY NETWORKS UNDER ELLIPSOIDAL UNCERTAINTY - DATA ANALYSIS AND
PREDICTION BY OPTIMIZATION THEORY AND DYNAMICAL SYSTEMS 27
ERIK KROPAT, GERHARD-WILHELM WEBER, CHANDRA SEKHAR PEDAMALLU 1
INTRODUCTION 27
2 ELLIPSOIDAL CALCULUS 30
2.1 ELLIPSOIDAL DESCRIPTIONS 30
2.2 AFFINE TRANSFORMATIONS 31
2.3 SUMS OF TWO ELLIPSOIDS 31
2.4 SUMS OF K ELLIPSOIDS 31
2.5 INTERSECTION OF ELLIPSOIDS 32
3 TARGET-ENVIRONMENT REGULATORY SYSTEMS UNDER ELLIPSOIDAL UNCERTAINTY 33
3.1 THE TIME-DISCRETE MODEL 33
3.2 ALGORITHM 37
4 THE REGRESSION PROBLEM 40
4.1 THE TRACE CRITERION 43
4.2 THE TRACE OF THE SQUARE CRITERION 43
4.3 THE DETERMINANT CRITERION 44
4.4 THE DIAMETER CRITERION 44
4.5 OPTIMIZATION METHODS 45
5 MIXED INTEGER REGRESSION PROBLEM 47
6 CONCLUSION 49
REFERENCES 50
CHAPTER 4
A VISUAL ENVIRONMENT FOR DESIGNING AND RUNNING DATA MINING WORKFLOWS IN
THE KNOWLEDGE GRID 57
EUGENIO CESARIO, MARCO LACKOVIC, DOMENICO TALIA, PAOLO TRUNFIO 1
INTRODUCTION 57
2 THE KNOWLEDGE GRID 58
3 WORKFLOW COMPONENTS 60
4 THE DIS3GNO SYSTEM 63
5 EXECUTION MANAGEMENT 65
6 USE CASES AND PERFORMANCE 67
6.1 PARAMETER SWEEPING WORKFLOW 67
6.2 ENSEMBLE LEARNING WORKFLOW 70
IMAGE 3
CONTENTS IX
7 RELATED WORK 72
8 CONCLUSIONS 74
REFERENCES 74
CHAPTER 5
FORMAL FRAMEWORK FOR THE STUDY OF ALGORITHMIC PROPERTIES OF OBJECTIVE
INTERESTINGNESS MEASURES 77
LE BRAS YANNICK, LENCA PHILIPPE, STEPHANE LALLICH 1 INTRODUCTION 77
2 SCIENTIFIC LANDSCAPE 79
2.1 DATABASE 79
2.2 ASSOCIATION RULES 81
2.3 INTERESTINGNESS MEASURES 82
3 A FRAMEWORK FOR THE STUDY OF MEASURES 83
3.1 ADAPTED FUNCTIONS OF MEASURE 84
3.2 EXPRESSION OF A SET OF MEASURES 87
4 APPLICATION TO PRUNING STRATEGIES 88
4.1 ALL-MONOTONY 89
4.2 UNIVERSAL EXISTENTIAL UPWARD CLOSURE 90
4.3 OPTIMAL RULE DISCOVERY 92
4.4 PROPERTIES VERIFIED BY THE MEASURES 94
CONCLUSION 94
REFERENCES 95
CHAPTER 6
NONNEGATIVE MATRIX FACTORIZATION: MODELS, ALGORITHMS AND APPLICATIONS 99
ZHONG-YUAN ZHANG 1 INTRODUCTION 99
2 STANDARD NMF AND VARIATIONS 101
2.1 STANDARD NMF 101
2.2 SEMI-NMF ([22]) 103
2.3 CONVEX-NMF ([22]) 103
2.4 TRI-NMF ([23]) 103
2.5 KERNEL NMF ([24]) 104
2.6 LOCAL NONNEGATIVE MATRIX FACTORIZATION, LNMF ([25,26]) 104
2.7 NONNEGATIVE SPARSE CODING, NNSC ([28]) 104
2.8 SPARES NONNEGATIVE MATRIX FACTORIZATION, SNMF ([29,30,31]) 104
2.9 NONNEGATIVE MATRIX FACTORIZATION WITH SPARSENESS CONSTRAINTS, NMFSC
([32]) 105
2.10 NONSMOOTH NONNEGATIVE MATRIX FACTORIZATION, NSNMF ([15]) 105
2.11 SPARSE NMFS: SNMF/R, SNMF/L ([33]) 106
IMAGE 4
X CONTENTS
2.12 CUR DECOMPOSITION ([34]) 106
2.13 BINARY MATRIX FACTORIZATION, BMF ([20,21]) 106
3 DIVERGENCE FUNCTIONS AND ALGORITHMS FOR NMF 106
3.1 DIVERGENCE FUNCTIONS 108
3.2 ALGORITHMS FOR NMF 109
4 APPLICATIONS OF NMF 115
4.1 IMAGE PROCESSING 115
4.2 CLUSTERING 116
4.3 SEMI-SUPERVISED CLUSTERING 116
4.4 BI-CLUSTERING (CO-CLUSTERING) 117
4.5 FINANCIAL DATA MINING 118
5 RELATIONS WITH OTHER RELEVANT MODELS 118
5.1 RELATIONS BETWEEN NMF AND K-MEANS 119
5.2 RELATIONS BETWEEN NMF AND PLSI 120
6 CONCLUSIONS AND FUTURE WORKS 126
APPENDIX 127
REFERENCES 131
CHAPTER 7
VISUAL DATA MINING AND DISCOVERY WITH BINARIZED VECTORS 135 BORIS
KOVALERCHUK, FLORIAN DELIZY, LOGAN RIGGS, EVGENII VITYAEV 1 INTRODUCTION
136
2 METHOD FOR VISUALIZING DATA 138
3 VISUALIZATION FOR BREAST CANCER DIAGNISTICS 145
4 GENERAL CONCEPT OF USING MDF IN DATA MINING 147
5 SCALING ALGORITHMS 148
5.1 ALGORITHM WITH DATA-BASED CHAINS 148
5.2 ALGORITHM WITH PIXEL CHAINS 149
6 BINARIZATION AND MONOTONIZATION 152
7 MONOTONIZATION 154
8 CONCLUSION 155
REFERENCES 155
CHAPTER 8
A N EW APPROACH AND ITS APPLICATIONS FOR TIME SERIES ANALYSIS AND
PREDICTION BASED ON MOVING AVERAGE OF N TH -ORDER DIFFERENCE 157
YANG LAN, DANIEL NEAGU 1 INTRODUCTION 157
2 DEFINITIONS RELEVANT TO TIME SERIES PREDICTION 159
3 THE ALGORITHM OF MOVING AVERAGE OF N"*-ORDER DIFFERENCE FOR BOUNDED
TIME SERIES PREDICTION 161
4 FINDING SUITABLE INDEX M AND ORDER LEVEL N FOR INCREASING THE
PREDICTION PRECISION 168
5 PREDICTION RESULTS FOR SUNSPOT NUMBER TIME SERIES 170
IMAGE 5
CONTENTS XI
6 PREDICTION RESULTS FOR EARTHQUAKE TIME SERIES 173
7 PREDICTION RESULTS FOR PSEUDO-PERIODICAL SYNTHETIC TIME SERIES 175
8 PREDICTION RESULTS COMPARISON 177
9 CONCLUSIONS 179
10 APPENDIX 180
REFERENCES 182
CHAPTER 9
EXCEPTIONAL MODEL MINING 183
ARNO KNOBBE, AD FEELDERS, DENNIS LEMAN 1 INTRODUCTION 183
2 EXCEPTIONAL MODEL MINING 185
3 MODEL CLASSES 187
3.1 CORRELATION MODELS 187
3.2 REGRESSION MODEL 188
3.3 CLASSIFICATION MODELS 189
4 EXPERIMENTS 192
4.1 ANALYSIS OF HOUSING DATA 192
4.2 ANALYSIS OF GENE EXPRESSION DATA 194
5 CONCLUSIONS AND FUTURE RESEARCH 197
REFERENCES 198
CHAPTER 10
ONLINE CHIMERGE ALGORITHM 199
PETRI LEHTINEN, MATTI SAARELA, TAPIO ELOMAA 1 INTRODUCTION 199
2 NUMERIC ATTRIBUTES, DECISION TREES, AND DATA STREAMS 201 2.1 VFDT AND
NUMERIC ATTRIBUTES 201
2.2 FURTHER APPROACHES * 202
3 CHIMERGE ALGORITHM 204
4 ONLINE VERSION OF CHIMERGE 205
4.1 TIME COMPLEXITY OF ONLINE CHIMERGE 208
4.2 ALTERNATIVE APPROACHES 209
5 A COMPARATIVE EVALUATION 210
6 CONCLUSION 213
REFERENCES 214
CHAPTER 11
MINING CHAINS OF RELATIONS 217
FOTO AFRATI, GAUTAM DAS, ARISTIDES GIONIS, HEIKKI MANNILA, TANELI
MIELIKAEINEN, PANAYIOTIS TSAPARAS 1 INTRODUCTION 217
2 RELATED WORK 219
3 THE GENERAL FRAMEWORK 220
IMAGE 6
XII CONTENTS
3.1 MOTIVATION 222
3.2 PROBLEM DEFINITION 223
3.3 EXAMPLES OF PROPERTIES 225
3.4 EXTENSIONS OF THE MODEL 227
4 ALGORITHMIC TOOLS 229
4.1 A CHARACTERIZATION OF MONOTONICITY 230
4.2 INTEGER PROGRAMMING FORMULATIONS 231
4.3 CASE STUDIES 233
5 EXPERIMENTS 238
5.1 DATASETS 238
5.2 PROBLEMS 239
6 CONCLUSIONS 241
REFERENCES 243
AUTHOR INDEX 247 |
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| indexdate | 2024-07-21T00:31:11Z |
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| isbn | 364223240X 9783642232404 |
| language | English |
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| physical | XII, 246 S. graph. Darst. 25 cm |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Springer |
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| series | Intelligent systems reference library |
| series2 | Intelligent systems reference library |
| spelling | Data mining foundations and intelligent paradigms 2 Statistical, Bayesian, time series and other theoretical aspects Dawn E. Holmes and Lakhmi C. Jain (eds.) Berlin ; Heidelberg Springer 2012 XII, 246 S. graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Intelligent systems reference library 24 Intelligent systems reference library ... Holmes, Dawn E. edt (DE-604)BV040111717 2 Erscheint auch als Online-Ausgabe 978-3-642-23241-1 Intelligent systems reference library 24 (DE-604)BV035704685 24 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3853472&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=024968036&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 2 Statistical, Bayesian, time series and other theoretical aspects Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_fullStr | Data mining foundations and intelligent paradigms 2 Statistical, Bayesian, time series and other theoretical aspects Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_full_unstemmed | Data mining foundations and intelligent paradigms 2 Statistical, Bayesian, time series and other theoretical aspects Dawn E. Holmes and Lakhmi C. Jain (eds.) |
| title_short | Data mining |
| title_sort | data mining foundations and intelligent paradigms statistical bayesian time series and other theoretical aspects |
| title_sub | foundations and intelligent paradigms |
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