Various data mining methods have been proposed to handle large-scale data and discover interesting knowledge hidden in the data. Maximal frequent pattern mining is one of the data mining techniques suggested to solve the fatal prob- lem of traditional frequent pattern mining approach. While
traditional approach may extract an enormous number of pat-tern results according to threshold settings, maximal frequent pattern mining approach mines a smaller number of repre-sentative patterns, which allow users to analyze given data more efficiently. In this paper, we describe various recent
maximal frequent pattern mining methods using additional factors and conduct performance evaluation in order to ana- lyze their detailed characteristics.

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