Domain-Driven Data Mining: A Practical Methodology

Domain-Driven Data Mining: A Practical Methodology

Longbing Cao (University of Technology, Sydney, Australia) and Chengqi Zhang (University of Technology, Sydney, Australia)
Copyright: © 2006 |Pages: 17
DOI: 10.4018/jdwm.2006100103
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Abstract

Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an isolated, case-by-case manner. As a result, very often the knowledge discovered generally is not interesting to real business needs. Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference. The domain-driven data mining consists of a DDID-PD framework that considers key components such as constraint-based context, integrating domain knowledge, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process. We also illustrate some examples in mining actionable correlations in Australian Stock Exchange, which show that domain-driven data mining has potential to improve further the actionability of patterns for practical use by industry and business.

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