Actionable Knowledge Discovery

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Author(s): Longbing Cao (University of Technology Sydney, Australia)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Information Science and Technology, Second Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour (Information Resources Management Association, USA)
DOI: 10.4018/978-1-60566-026-4.ch002
ISBN13: 9781605660264
ISBN10: 1605660264
EISBN13: 9781605660271

Keywords: Information Resources Management / Information Science Reference / Library and Information Science

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Abstract

Actionable knowledge discovery is selected as one of the greatest challenges (Ankerst, 2002; Fayyad, Shapiro, & Uthurusamy, 2003) of next-generation knowledge discovery in database (KDD) studies (Han & Kamber, 2006). In the existing data mining, often mined patterns are nonactionable to real user needs. To enhance knowledge actionability, domain-related social intelligence is substantially essential (Cao et al., 2006b). The involvement of domain-related social intelligence into data mining leads to domaindriven data mining (Cao & Zhang, 2006a, 2007a), which complements traditional data-centered mining methodology. Domain-related social intelligence consists of intelligence of human, domain, environment, society and cyberspace, which complements data intelligence. The extension of KDD toward domain-driven data mining involves many challenging but promising research and development issues in KDD. Studies in regard to these issues may promote the paradigm shift of KDD from data-centered interesting pattern mining to domain-driven actionable knowledge discovery, and the deployment shift from simulated data set-based to real-life data and business environment-oriented as widely predicted.

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