TY  - BOOK
AU  - Cao,Longbing
AU  - Yu,Philip S.
AU  - Zhang,Chengqi
AU  - Zhao,Yanchang
ED  - SpringerLink (Online service)
TI  - Domain Driven Data Mining
SN  - 9781441957375
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2010///
CY  - Boston, MA
PB  - Springer US
KW  - Computer science
KW  - Data mining
KW  - Information storage and retrieval systems
KW  - Information systems
KW  - Management information systems
KW  - Computer Science
KW  - Data Mining and Knowledge Discovery
KW  - Business Information Systems
KW  - Information Systems Applications (incl.Internet)
KW  - Information Storage and Retrieval
N1  - Challenges and Trends -- Methodology -- Ubiquitous Intelligence -- Knowledge Actionability -- AKD Frameworks -- Combined Mining -- Agent-Driven Data Mining -- Post Mining -- Mining Actionable Knowledge on Capital Market Data -- Mining Actionable Knowledge on Social Security Data -- Open Issues and Prospects -- Reading Materials
N2  - In the present thriving global economy a need has evolved for complex data analysis to enhance an organization’s production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. About this book: Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence. Examines real-world challenges to and complexities of the current KDD methodologies and techniques. Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications. Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications Includes techniques, methodologies and case studies in real-life enterprise data mining Addresses new areas such as blog mining Domain Driven Data Mining is suitable for researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management
UR  - http://dx.doi.org/10.1007/978-1-4419-5737-5
ER  -