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.
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This work is sponsored by UTS Chancellor research fund and ECRG Fund.
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Cao, L., Zhang, C. (2006). Domain-Driven Actionable Knowledge Discovery in the Real World. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_96
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DOI: https://doi.org/10.1007/11731139_96
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