Abstract: In Data Mining, Association rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. the usefulness of association rules is vigorous limited by the huge amount of delivered rules. To overcome this drawback, several methods were proposed in the literature such as itemset concise representations, redundancy reduction, and postprocessing. Although, being generally based on statistical report, most of these methods do not guarantee that the extracted rules are interesting for the user. Thus, it is critical to help the decision-maker with an efficient postprocessing step in order to reduce the number of rules. This paper proposes a new interactive approach to prune and filter discovered rules. First, it propose to use ontologies in order to improve the integration of user knowledge in the postprocessing task. Second, it proposes the Rule internal representation of formalism extending the specification language proposed by Liu et al. for user expectations. Third it proposes to use the same in large databases for an effective and efficient result with out loss of an interesting item set. This paper system will reduce the number of rules with out loss an interesting item set while dealing with Large Databases.
Keywords: Association rules, classification, interactive data exploration and discovery , Post processing Clustering