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
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
Postprocessing Decision Trees to Extract Actionable Knowledge
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
Domain-Driven, Actionable Knowledge Discovery
IEEE Intelligent Systems
Developing Actionable Trading Strategies for Trading Agents
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Multi-strategy Integration for Actionable Trading Agents
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
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 actionable knowledge discovery in the real world
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Activity mining: challenges and prospects
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Applications of Data Mining in E-Business Finance: Introduction
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Towards the Generic Framework for Utility Considerations in Data Mining Research
Proceedings of the 2010 conference on Data Mining for Business Applications
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From the evolution of developing a pattern interestingness perspective, data mining has experienced two phases, which are Phase 1: technical objective interestingness focused research, and Phase 2: technical objective and subjective interestingness focused studies. As a result of these efforts, patterns mined are of significant interest to technical concern. However, technically interesting patterns are not necessarily of interest to business. In fact, real-world experience shows that many mined patterns, which are interesting from the perspective of the data mining method used, are out of business expectations when they are delivered to the final user. This scenario actually involves a grand challenge in next-generation KDD (Knowledge Discovery in Databases) studies, defined as actionable knowledge discovery. To discover knowledge that can be used for taking actions to business advantages, this paper addresses a framework that extends the evolution process of knowledge evaluation to Phase 3 and Phase 4. In Phase 3, concerns with objective interestingness from a business perspective are added on top of Phase 2, while in Phase 4 both technical and business interestingness should be satisfied in terms of objective and subjective perspectives. The introduction of Phase 4 provides a comprehensive knowledge actionability framework for actionable knowledge discovery. We illustrate applications in governmental data mining showing that the considerations and adoption of the framework described in Phase 4 has potential to enhance both sides of interestingness and expectation. As a result, knowledge discovered has better chances to support action-taking in the business world.