Data mining and knowledge discovery in databases
Communications of the ACM
Issues in visualizing large databases
Proceedings of the third IFIP WG2.6 working conference on Visual database systems 3 (VDB-3)
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Patterns That Respond to Actions
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Extracting Actionable Knowledge from Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
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
Mining Non-redundant Reclassification Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Domain driven data mining to improve promotional campaign ROI and select marketing channels
Proceedings of the 18th ACM conference on Information and knowledge management
Mining action rules from scratch
Expert Systems with Applications: An International Journal
A distance-based approach for action recommendation
ECML'05 Proceedings of the 16th European conference on Machine Learning
Post mining of diversified multiple decision trees for actionable knowledge discovery
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Mining actionable behavioral rules
Decision Support Systems
An integrative framework for intelligent software project risk planning
Decision Support Systems
Causality-based cost-effective action mining
Intelligent Data Analysis
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Most data mining algorithms and tools stop at discoveredcustomer models, producing distribution informationon customer profiles. Such techniques, when applied to industrialproblems such as customer relationship management(CRM), are useful in pointing out customers who arelikely attritors and customers who are loyal, but they requirehuman experts to postprocess the mined information manually.Most of the postprocessing techniques have been limitedto producing visualization results and interestingnessranking, but they do not directly suggest actions that wouldlead to an increase the objective function such as profit. Inthis paper, we present a novel algorithm that suggest actionsto change customers from an undesired status (suchas attritors) to a desired one (such as loyal) while maximizingobjective function: the expected net profit. We developthese algorithms under resource constraints that areabound in reality. The contribution of the work is in takingthe output from an existing mature technique (decisiontrees, for example), and producing novel, actionable knowledgethrough automatic postprocessing.