C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Learning plans without a priori knowledge
Adaptive Behavior
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Planning and Acting in Partially Observable Stochastic Domains
Planning and Acting in Partially Observable Stochastic Domains
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Computational aspects of mining maximal frequent patterns
Theoretical Computer Science
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We consider the problem of mining high-utility plansfrom historical plan databases that can be used to transformcustomers from one class to other, more desirable classes.Traditional data mining algorithms are focused on findingfrequent sequences. But high frequency may not imply lowcosts and high benefits. Traditional Markov Decision Process(MDP) algorithms are designed to address this issueby bringing in the concept of utility, but these algorithmsare also known to be expensive to execute. In this paper,we present a novel algorithm AUPlan which automaticallygenerates sequential plans with high utility by combiningdata mining and AI planning. These high-utility plans couldbe used to convert groups of customers from less desirablestates to more desirable ones. Our algorithm adapts theApriori algorithm by considering the concepts of plans andutilities. We show through empirical studies that planningusing our integrated algorithm produces high-utility plansefficiently.