Abstract

Mining infrequent Itemset is fundamental method for mining association rules as well as for many other frequent Itemset mining tasks. In existing methods for Mining frequent and infrequent has been implemented using FP growth or Apriori algorithm. Also numerous experimental results have demonstrated that these techniques are scalable to the mining process. In this paper, a novel Transaction Mapping Technique for filtering the association rules for infrequent Itemset in the transaction dataset is proposed. This Proposed technique produces improved performance for sparse data items. Furthermore, an open and closed Itemset extraction of rules using optimization techniques is presented. The experimental results prove that this proposed technique is highly scalable and consume less memory compared to the state of art techniques.

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