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Domain-Driven Actionable Knowledge Discovery in the Real World

  • Longbing Cao
  • Chengqi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

Actionable knowledgediscovery is one of Grand Challenges in KDD. To this end, many methodologies have been developed. However, they either view data mining as an autonomous data-driven trial-and-error process, or only analyze the issues in an isolated and case-by-case manner. As a result, the knowledge discovered is often not actionable to constrained business. This paper proposes a practical perspective, referred to as domain-driven in-depth pattern discovery (DDID-PD). It presents a domain-driven view of discovering knowledge satisfying real business needs. Its main ideas include constraint mining, in-depth mining, human-cooperated mining, and loop-closed mining. We demonstrate its deployment in mining actionable trading strategies in Australian Stock Exchange data.

Keywords

Data Mining Domain Knowledge Domain Expert Trading Strategy Actionable Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Longbing Cao
    • 1
  • Chengqi Zhang
    • 1
  1. 1.Faculty of Information TechnologyUniversity of Technology SydneyAustralia

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