Systems support for scalable data mining
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Towards effective and interpretable data mining by visual interaction
ACM SIGKDD Explorations Newsletter
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Report on the SIGKDD-2002 panel the perfect data mining tool: interactive or automated?
ACM SIGKDD Explorations Newsletter
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Summary from the KDD-03 panel: data mining: the next 10 years
ACM SIGKDD Explorations Newsletter
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Agents and data mining: mutual enhancement by integration
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Data mining for lifetime prediction of metallic components
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Mining in-depth patterns in stock market
International Journal of Intelligent Systems Technologies and Applications
Towards Business Interestingness in Actionable Knowledge Discovery
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Domain-Driven Data Mining: Methodologies and Applications
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Domain driven data mining to improve promotional campaign ROI and select marketing channels
Proceedings of the 18th ACM conference on Information and knowledge management
Fuzzy genetic algorithms for pairs mining
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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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.