Abstract
Frequent Itemset (FIs) Mining is the most popular data mining technique and fundamental task for association rule and correlation mining. Closed Frequent Itemsets (CFIs) provide a lossless compact representation of Frequent Itemsets. Although many algorithms exist for mining Frequent Itemsets and Closed Frequent Itemsets, they lack human involvement for better guidance and control. Constraint-based data mining enables users to add their constraints along with the standard rules of the algorithm to suit their need. In this paper, we propose an effective constraint variable named Subset Significance Threshold (SST) which can be used along with minimum support threshold to mine significant CFIs. The CFIs which do not satisfy the constraint are considered insignificant and eliminated. Experiment analysis on various representational datasets proved the proposed constraint variable is effective in identifying insignificant CFIs.
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