Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Action-Rules: How to Increase Profit of a Company
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Action rules mining: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Action rules discovery, a new simplified strategy
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Action rules discovery system DEAR_3
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
An expected utility-based approach for mining action rules
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
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Action rules can be seen as an answer to the question: what one can do with results of data mining and knowledge discovery? Some applications include: medical field, e-commerce, market basket analysis, customer satisfaction, and risk analysis. Action rules are logical terms describing knowledge about possible actions associated with objects, which is hidden in a decision system. Classical strategy for discovering them from a database requires prior extraction of classification rules which next are evaluated pair by pair with a goal to suggest an action, based on condition features in order to get a desired effect on a decision feature. An actionable strategy is represented as a term $r = [(\omega) \wedge (\alpha \rightarrow \beta)] \Rightarrow [\phi \rightarrow \psi]$, where ï戮驴, ï戮驴, β, ï戮驴, and ï戮驴are descriptions of objects or events. The term rstates that when the fixed condition ï戮驴is satisfied and the changeable behavior (ï戮驴ï戮驴β) occurs in objects represented as tuples from a database so does the expectation (ï戮驴ï戮驴ï戮驴). With each object a number of actionable strategies can be associated and each one of them may lead to different expectations and the same to different re-classifications of objects. In this paper we will focus on a new strategy of constructing action rules directly from single classification rules instead of pairs of classification rules. It presents a gain on the simplicity of the method of action rules construction, as well as on its time complexity. We present A*-type heuristic strategy for discovering only interesting action rules, which satisfy user-defined constraints such as: feasibility, maximal cost, and minimal confidence. We, therefore, propose a new method for fast discovery of interesting action rules.