Interactive concept description with Bayesian partition models: an approach for the analysis of hierarchically structured quality data in the automotive industry
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis/Dissertation Book |
| Language: | English |
| Published: |
[Amsterdam u.a.]
IOS Pr.
2010
Heidelberg AKA |
| Series: | Dissertationen zur künstlichen Intelligenz
330 |
| Subjects: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020596820&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Item Description: | Zsfassung in engl. und dt. Sprache |
| Physical Description: | XV, 206 S. Ill., graph. Darst. |
| ISBN: | 9783898383301 9781607506249 |
Staff View
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| 245 | 1 | 0 | |a Interactive concept description with Bayesian partition models |b an approach for the analysis of hierarchically structured quality data in the automotive industry |c Markus Müller |
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| adam_text | CONTENTS CONTENTS XIII 1 INTRODUCTION 1 1.1 APPLICATION CONTEXT 2 1.2
DATA MINING FOR QUALITY ANALYSIS 3 1.3 OUTLINE 5 2 KNOWLEDGE DISCOVERY
IN QUALITY DATA 7 2.1 KNOWLEDGE DISCOVERY IN DATABASES 7 2.1.1 CRISP-DM
PROCESS MODEL 8 2.1.2 DATA MINING PROBLEM TYPES 10 2.2 DATA WAREHOUSE
SYSTEMS 13 2.2.1 FUNDAMENTALS 13 2.2.2 MULTI-DIMENSIONAL DATA MODEL 14
2.3 DATA WAREHOUSING AND DATA MINING 18 2.3.1 DATA WAREHOUSE SYSTEM AS
DATA PROVIDER 19 2.3.2 OLAP MINING 20 2.3.3 DISCOVERY-DRIVEN OLAP 21 2.4
STATISTICAL MODELING 22 2.4.1 BASICS 23 2.4.2 RANDOM VARIABLES AND
PROBABILITY FUNCTIONS 27 2.4.3 BASIC PROBABILITY DISTRIBUTIONS 30 2.4.4
EXCHANGEABILITY AND RANDOM SAMPLING 32 2.4.5 SUMMARY OF NOTATION 33 2.5
CONCEPT LEARNING AND DESCRIPTION 33 2.5.1 TERMINOLOGY 34 2.5.2 DEFINING
AND LEARNING CONCEPTS 36 2.5.3 FORMAL PROBLEM DESCRIPTION 39 XIII
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1007194995 DIGITALISIERT
DURCH XRV CONTENTS 2.5.4 QUALITY OF CONCEPT DESCRIPTIONS 40 2.5.5
APPLICATION TO QUALITY ANALYSIS 44 3 RELATED WORK 47 3.1 CONCEPT
DESCRIPTION 47 3.1.1 DECISION TREES 48 3.1.2 SUBGROUP DISCOVERY 51 3.1.3
RULE CUBES 55 3.1.4 OLAP 57 3.2 CAUSALMODELS 59 3.2.1 BAYESIAN NETWORKS
59 3.2.2 INFERENCE WITH BAYESIAN NETWORKS 63 3.2.3 PARAMETER ESTIMATION
64 3.2.4 STRUCTURE LEARNING 67 3.2.5 BAYESIAN CLASSIFIERS 70 3.2.6
DISCUSSION 72 4 BAYESIAN PARTITION MODELS 75 4.1 BAYESIAN MODEL
SELECTION 75 4.1.1 STATISTICAL INFERENCE 76 4.1.2 MODEL FITTING 76 4.1.3
MODEL COMPARISON 84 4.2 PRODUCT PARTITION MODELS 88 4.2.1 GENERAL MODEL
88 4.2.2 APPLICATION TO CONCEPT DESCRIPTION 89 4.3 HIERARCHICAL BAYESIAN
PARTITION MODELS 91 4.4 HYBRID APPROACH 94 4.5 ASSESSMENT OF PARTITIONS
98 4.5.1 SCHWARZ CRITERION 99 4.5.2 EXPERIMENTAL EVALUATION 100 4.5.3
CONCLUSION 109 4.6 COMPARISON OF PARTITION MODELS AND BAYESIAN NETWORKS
110 4.7 STATISTICS AND CAUSALITY 112 4.7.1 RANDOM COINCIDENCE 112 4.7.2
CONFOUNDING VARIABLES 113 4.7.3 SIMPSON S PARADOX 116 4.7.4 DIRECTION OF
CAUSALITY 118 4.7.5 POLICY-MAKING 118 4.7.6 CONCLUSION 119 5 BAYESIAN
PARTITION MODELS FOR CONCEPT DESCRIPTION 121 5.1 ASSUMPTIONS 121 5.
CONTENTS XV 5.3.1 HIERARCHICALLY STRUCTURED, CATEGORICAL VARIABLES 124
5.3.2 SIMPLE CATEGORICAL VARIABLES 128 5.3.3 NUMERIC AND ORDINAL
VARIABLES 129 5.4 PARTITION MATRIX 130 5.4.1 INTERACTIONS OF TWO BINARY
PARTITIONS 131 5.4.2 SIMILAR VARIABLES 134 5.5 IMPLEMENTATION 137 5.5.1
A SOFTWARE FOR INTEGRATED QUALITY MINING 137 5.5.2 INTEGRATION OF
INTERACTIVE LOOK-AHEAD DECISION TREES 138 6 EVALUATION 145 6.1 RUNTIME
146 6.1.1 BINARY ATTRIBUTE INTERACTIONS 146 6.1.2 GROUPING OF
CATEGORICAL AND NUMERIC ATTRIBUTES 146 6.1.3 TAXONOMIES 148 6.1.4
CONCLUSION 150 6.2 EVALUATION ON SIMULATED DATA 150 6.2.1
PSEUDO-INFLUENCES AND TAXONOMIES 150 6.2.2 LOGICALXOR 153 6.2.3
SIMPSON S PARADOX 155 6.3 EVALUATION OF INTERESTINGNESS MEASURES 156 6.4
COMPARISON OF APPROACHES 160 6.4.1 DATA SET DESCRIPTION 161 6.4.2
INTERACTIVE LOOK-AHEAD TREES 162 6.4.3 DECISION TREES 166 6.4.4 BAYESIAN
NETWORK LEARNING 170 6.4.5 ASSOCIATION RULES 172 6.4.6 RULE CUBES 173
6.5 REAL-WORLD CASE STUDIES 176 6.5.1 OXYGEN SENSOR 177 6.5.2 SEAT
OCCUPANCY RECOGNITION 182 7 CONCLUSION 185 A SYMBOLS 191 BIBLIOGRAPHY
193 INDEX 203
|
| any_adam_object | 1 |
| author | Müller, Markus |
| author_GND | (DE-588)142350850 |
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| discipline | Maschinenbau / Maschinenwesen Informatik Mathematik Wirtschaftswissenschaften |
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| id | DE-604.BV036677885 |
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| indexdate | 2024-12-20T14:39:37Z |
| institution | BVB |
| isbn | 9783898383301 9781607506249 |
| language | English |
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| physical | XV, 206 S. Ill., graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | IOS Pr. AKA |
| record_format | marc |
| series | Dissertationen zur künstlichen Intelligenz |
| series2 | Dissertationen zur künstlichen Intelligenz |
| spellingShingle | Müller, Markus Interactive concept description with Bayesian partition models an approach for the analysis of hierarchically structured quality data in the automotive industry Dissertationen zur künstlichen Intelligenz Wissensextraktion (DE-588)4546354-2 gnd Kausalanalyse (DE-588)4163511-5 gnd Data Mining (DE-588)4428654-5 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Kraftfahrzeugindustrie (DE-588)4032690-1 gnd Qualitätsdaten (DE-588)4418688-5 gnd Begriffslernen (DE-588)4144315-9 gnd |
| subject_GND | (DE-588)4546354-2 (DE-588)4163511-5 (DE-588)4428654-5 (DE-588)4144220-9 (DE-588)4032690-1 (DE-588)4418688-5 (DE-588)4144315-9 (DE-588)4113937-9 |
| title | Interactive concept description with Bayesian partition models an approach for the analysis of hierarchically structured quality data in the automotive industry |
| title_auth | Interactive concept description with Bayesian partition models an approach for the analysis of hierarchically structured quality data in the automotive industry |
| title_exact_search | Interactive concept description with Bayesian partition models an approach for the analysis of hierarchically structured quality data in the automotive industry |
| title_full | Interactive concept description with Bayesian partition models an approach for the analysis of hierarchically structured quality data in the automotive industry Markus Müller |
| title_fullStr | Interactive concept description with Bayesian partition models an approach for the analysis of hierarchically structured quality data in the automotive industry Markus Müller |
| title_full_unstemmed | Interactive concept description with Bayesian partition models an approach for the analysis of hierarchically structured quality data in the automotive industry Markus Müller |
| title_short | Interactive concept description with Bayesian partition models |
| title_sort | interactive concept description with bayesian partition models an approach for the analysis of hierarchically structured quality data in the automotive industry |
| title_sub | an approach for the analysis of hierarchically structured quality data in the automotive industry |
| topic | Wissensextraktion (DE-588)4546354-2 gnd Kausalanalyse (DE-588)4163511-5 gnd Data Mining (DE-588)4428654-5 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Kraftfahrzeugindustrie (DE-588)4032690-1 gnd Qualitätsdaten (DE-588)4418688-5 gnd Begriffslernen (DE-588)4144315-9 gnd |
| topic_facet | Wissensextraktion Kausalanalyse Data Mining Bayes-Entscheidungstheorie Kraftfahrzeugindustrie Qualitätsdaten Begriffslernen Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020596820&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV005345280 |
| work_keys_str_mv | AT mullermarkus interactiveconceptdescriptionwithbayesianpartitionmodelsanapproachfortheanalysisofhierarchicallystructuredqualitydataintheautomotiveindustry |
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Branch Library Mathematics & Informatics
| Call Number: |
0102 DAT 700d 2001 A 894-330
Floor plan |
|---|---|
| Copy 1 | Available for loan On Shelf |