Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Extracting Actionable Knowledge from Decision Trees
IEEE Transactions on Knowledge and Data Engineering
A Profit-Based Business Model for Evaluating Rule Interestingness
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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
Interpreting concept learning in cognitive informatics and granular computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Mining action rules from scratch
Expert Systems with Applications: An International Journal
Explanation oriented association mining using rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
New prediction model for pre-fetching in mobile database
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
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
An expected utility-based approach for mining action rules
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
Mining actionable behavioral rules
Decision Support Systems
An integrative framework for intelligent software project risk planning
Decision Support Systems
Mining Meta-Actions for Action Rules Reduction
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
| Hi-index | 0.00 |
Data mining has been applied to CRM (Customer RelationshipManagement) in many industries witha limitedsuccess.Most data mining tools can only discover customer modelsor profiles (such as customers who are likely attritors andcustomers who are loyal), but not actions that would improvecustomer relationship (such as changing attritors toloyal customers). We describe a novel algorithm that suggestsactions to change customers from an undesired status(such as attritors) to a desired one (such as loyal). Our algorithmtakes into account the cost of actions, and further,it attempts to maximize the expected net profit. To our bestknowledge, no data mining algorithms or tools today can accomplishthis important task in CRM. The algorithm is implemented,with many advanced features, in a specializedand highly effective data mining software called ProactiveSolution.