ABSTRACT
Among various problems in data mining, discovering maximal frequent itemsets is the most crucial one. Mining all the frequent itemsets will generate big amount of itemsets. Frequent maximal itemsets (FMIs) results in a much smaller number of itemsets. Hence, it is highly valuable to explore maximal frequent itemsets. In general, exploration of frequent itemsets has been implemented using a special data structure called LP-tree (Linear Prefix-tree). The presentation of LP-tree is in array form which reduces the usage of pointers among nodes. Linear prefix tree uses less information and linearly accesses corresponding nodes. In this research paper, a novel technique is designed called LP-MFI-tree for mining maximal frequent itemsets (MFI) which is extended from LP-growth method. It reduces memory consumption and runtime. Then, the performances of the LP-MFI-tree are validated through various experiments on different datasets.
