Closed itemset mining plays an important role in mining compressed representations or summaries of the set of frequent patterns. It uses the support information of itemsets and the superset–subset relationship among itemsets for removing redundancy. Closed itemset is always smaller or equal in cardinality comparing to other concise representation such as frequent free sets and generators. But, the compression using this closed-pattern approach may not be very effective, since slightly different count often exists between super and sub patterns. This paper focuses to compare two closed itemset algorithms such as FPclose and CFPtree-closed and a frequent itemset algorithm NCFPGEN for studying and determining the performance of these algorithms in the execution time aspect.

frequent itemset, closed itemset

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