DocumentCode
1091467
Title
Domain-Driven, Actionable Knowledge Discovery
Author
Longbing Cao ; Chengqi Zhang
Author_Institution
Univ. of Technol., Sydney
Volume
22
Issue
4
fYear
2007
Firstpage
78
Abstract
Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.
Keywords
data mining; business expectations; business intelligence; domain-driven data mining; knowledge discovery; Data mining; Data privacy; Data security; Data visualization; Government; Humans; Intelligent networks; Intelligent systems; Machine vision; Research and development; data mining; data models; database searching; knowledge engineering; visualization;
fLanguage
English
Journal_Title
Intelligent Systems, IEEE
Publisher
ieee
ISSN
1541-1672
Type
jour
DOI
10.1109/MIS.2007.67
Filename
4287277
Link To Document