Bridging the gap between business objectives and parameters of data mining algorithms
Decision Support Systems - Special issue: knowledge discovery and its applications to business decision making
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Towards effective and interpretable data mining by visual interaction
ACM SIGKDD Explorations Newsletter
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Profit Mining: From Patterns to Actions
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Item selection by "hub-authority" profit ranking
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Handbook of data mining and knowledge discovery
Postprocessing Decision Trees to Extract Actionable Knowledge
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
MPIS: Maximal-Profit Item Selection with Cross-Selling Considerations
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Agent Services-Based Infrastructure for Online Assessment of Trading Strategies
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Action rules mining: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Mining Actionable Patterns by Role Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Maximum profit mining and its application in software development
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain-Driven, Actionable Knowledge Discovery
IEEE Intelligent Systems
Mining in-depth patterns in stock market
International Journal of Intelligent Systems Technologies and Applications
Mining Impact-Targeted Activity Patterns in Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering
Fuzzy genetic algorithms for pairs mining
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Intelligence metasynthesis in building business intelligence systems
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Towards Business Interestingness in Actionable Knowledge Discovery
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.