Discovering Action Rules That Are Highly Achievable from Massive Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining action rules from scratch
Expert Systems with Applications: An International Journal
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
From music to emotions and tinnitus treatment, initial study
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
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Corporations and institutions are often interested inderiving marketing strategies from corporate data andproviding informed advice for their customers oremployees. For example, a financial institution mayderive marketing strategies for turning their reluctantcustomers into active ones and a telecommunicationscompany may plan actions to stop their valuablecustomers from leaving. In data mining terms, theseadvice and action plans are aimed at convertingindividuals from an undesirable class to a desirable one,or to help devising a direct-marketing plan in order toincrease the profit for the institution. In this paper, wepresent an approach to use role models' for generatingsuch advice and plans. These role models are typicalcases that form a case base and can be used forcustomer advice generation. For each new customerseeking advice, a nearest-neighbor algorithm is used tofind a cost-effective and highly probable plan forswitching a customer to the most desirable role models.In this paper, we explore the tradeoff among time, spaceand quality of computation in this case-based reasoningframework. We demonstrate the effectiveness of themethods through empirical results.