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Mining of maximal frequent patterns is a basic problem in data mining applications. Small and useful association rules can be generated from maximal frequent itemset. The algorithms which are used to generate the maximal frequent patterns must perform efficiently. Most of the existing algorithms passed all frequent itemsets as candidates to the recursive algorithm which generates MFI. But the sparse dataset has huge number of frequent items and each frequent item has very small number of candidate items. This paper presents FastMFIMiner algorithm to generate MFI quickly from sparse dataset.