An Interactive Approach for Association Rules Using Ontologies for Knowledge-Based Interactive Post Mining

Abstract

In Data Mining, the association rules are strongly limited by the vast amount of rules were proposed in the research such as item set concise representations, post processing and redundancy reduction. It is based on statistical information of these methods the extracted rules are interesting for the user with an efficient post processing step in order to reduce the number of rules. An Interactive Approach for Association Rules Using Ontologies for Knowledge-Based Interactive PostMining proposes an interactive approach to prune and filter the discovered rules. First, propose ontologies in order to improve the integration of user knowledge in the postprocessing task. Next, propose the Rule Schema formalism extending the specification language is planned to assist the user throughout the examining task in post processing step, to reduce the rules. The quality of the filtered rules was validated by the domain expert at various points in the interactive process.

Keywords-Data Mining,PostMining, Association Rule, ARIPSO, Ontology, Pruning