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 :
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