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In this paper we present a novel hybrid (array-based layout and vertical bitmap layout) database representation approach for mining complete maximal frequent itemset (MFI) on sparse and large datasets. Our work is novel in terms of scalability, item search order and two horizontal and vertical projection techniques. We also present a maximal algorithm using this hybrid database representation approach. Different experimental results on real and sparse benchmark datasets show that our approach is better than previous state of art maximal algorithms

HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach