Domain-driven mining: Methodologies and applications

The aims and objectives of data mining is to discover actionable knowledge of maininterest to real user needs, which is one of Grand Challenges in KDD. Most extant data miningis a data-driven trial-an-error process. Patterns discovered via predefined models in the aboveprocess are often of limited interest to constraint-based real business. In order to work outpatterns really interesting and actionable to the real world, pattern discovery is more likely to bea domain-driven human-machine-cooperated process. This talk proposes II practical data miningmethodology named "domain-driven data mining". The main ideas include a Domain-DrivenIn-Depth Pattern Discovery framework (DDID-PD), constraint-based mining, in-depth mining,human-cooperated mining and loop-closed mining. Guided by this methodology, wedemonstrate some of our work in identifying useful correlations in real stock markets, forinstance, discovering optimal trading rules from the existing rule classes, and mining tradingrule-stock correlations in stock exchange data. The results have attracted strong interest fromboth traders and researchers in stock markets. It has shown that the methodology is potential forguiding deep mining of patterns interesting to real business.