A New Algorithm for Mining Maximal Frequent Patterns

Because of the inherent computational complexity, mining the complete set of frequent patterns remains to be a difficult task. Mining maximal frequent patterns is an alternative to address the problem. In this paper, a new algorithm, MOP, for mining maximal frequent patterns is proposed based on the Opportune Project algorithm proposed by the author in the previous study. MOP employs a breadth first and depth first combined hybrid search strategy to discover the frequent patterns, chooses different representations and projecting methods for transaction subsets in accordance with the features of the subsets, and integrates pruning methods based on closure checking and general subsumption checking. MOP also looks ahead opportunistically to discover maximal frequent patterns as early as possible. Both the search and pruning efficiency of MOP are maximized. Comparative experiments on real world and artificial datasets show that MOP outperforms MaxMiner by a factor of two to eight, and is more than two orders of magnitude efficient than MAFIA.