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Domain Driven Data Mining

:	Longbing Cao, Philip S. Yu, Chengqi Zhang, Yanchang Zhao
:	Domain Driven Data Mining
:	Springer-Verlag
:	9781441957375
:	1
:	CHF 85.90
:

:	Informatik
:	English

:	248
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.

	Preface	6
	Acknowledgements	8
	Contents	9
	Challenges and Trends	15
	1.1 Introduction	15
	1.2 KDD Evolution	16
	1.3 Challenges and Issues	17
	1.4 KDD Paradigm Shift	25
	1.5 Towards Domain Driven Data Mining	31
	1.6 Summary	39
	D3M Methodology	40
	2.1 Introduction	40
	2.2 D3M Methodology Concept Map	40
	2.3 D3M Key Components	41
	2.4 D3M Methodological Framework	53
	2.5 Summary	60
	Ubiquitous Intelligence	61
	3.1 Introduction	61
	3.2 Data Intelligence	61
	3.3 Domain Intelligence	67
	3.4 Network Intelligence	71
	3.5 Human Intelligence	74
	3.6 Organizational Intelligence	77
	3.7 Social Intelligence	79
	3.8 Involving Ubiquitous Intelligence	81
	3.9 Summary	84
	Knowledge Actionability	86
	4.1 Introduction	86
	4.2 Why Knowledge Actionability	87
	4.3 RelatedWork	88
	4.4 Knowledge Actionability Framework	89
	4.5 Aggregating Technical and Business Interestingness	98
	4.6 Summary	101
	D3M AKD Frameworks	103
	5.1 Introduction	103
	5.2 Why AKD Frameworks	104
	5.3 RelatedWork	106
	5.4 A System View of Actionable Knowledge Discovery	107
	5.5 Actionable Knowledge Discovery Frameworks	111
	5.6 Case Studies	119
	5.7 Discussions	120
	5.8 Summary	122
	Combined Mining	123
	6.1 Introduction	123
	6.2 Why Combined Mining	124
	6.3 Problem Statement	127
	6.4 The Concept of Combined Mining	131
	6.5 Multi-Feature Combined Mining	136
	6.6 Multi-Method Combined Mining	142
	6.7 Case Study: Mining Combined Patterns in E-Government Service Data	149
	6.8 RelatedWork	149
	6.9 Summary	152
	Agent-Driven Data Mining	154
	7.1 Introduction	154
	7.2 Complementation between Agents and Data Mining	154
	7.3 The Field of Agent Mining	156
	7.4 Why Agent-Driven Data Mining	159
	7.5 What Can Agents Do for Data Mining?	161
	7.6 Agent-Driven Distributed Data Mining	163
	7.7 Research Issues in Agent Driven Data Mining	168
	7.8 Case Study 1: F-Trade – An Agent-Mining Symbiont for Financial Services	169
	7.9 Case Study 2: Agent-based Multi-source Data Mining	170
	7.10 Case Study 3: Agent-based Adaptive Behavior Pattern Mining by HMM	171
	7.11 Research Resources on Agent Mining	176
	7.12 Summary	177
	Post Mining	179
	8.1 Introduction	179
	8.2 Interestingness Measures	180
	8.3 Filtering and Pruning	182
	8.4 Visualisation	184
	8.5 Summarization and Representation	185
	8.6 Post-Analysis	186
	8.7 Maintenance	187
	8.8 Summary	188
	Mining Actionable Knowledge on Capital Market Data	189
	9.1 Case Study 1: Extracting Actionable Trading Strategies	189
	9.2 Case Study 2: Mining Actionable Market Microstructure Behavior Patterns	204
	Mining Actionable Knowledge on Social Security Data	210
	10.1 Case Study: Mining Actionable Combined Associations	210
	10.2 Experiments: Mining Actionable Combined Patterns	214
	10.3 Summary	222
	Open Issues and Prospects	223
	11.1 Open Issues	223
	11.2 Trends and Prospects	224
	Reading Materials	226
	12.1 Activities on D3M	226
	12.2 References on D3M	227
	12.3 References on Agent Mining	228
	12.4 References on Post-analysis and Post-mining	228
	Glossary	229
	Reference	237
	Index	248