Association Rule Based Closed Frequent Item Set Algorithm (CFIA) For Chess Data Set

Taxonomy over the items in database can be more initiative and informative in the area of knowledge discovery. Association Rule Mining (ARM) focuses on finding a set of all subsets of items called item sets. This paper proposes a new approach for closed item sets using association rules. A smaller set of closed item sets can be a substitute of a larger item set. A new algorithm to mine a set of closed frequent item set called CFIA is presented as a part of this paper. The CFIA algorithm outperforms the traditional approaches of mining frequent item sets especially when the item sets intensity is dense in the database or when there are complicated permutations, probability of occurrences like Chess datasets