Performance and characteristic analysis of maximal frequent pattern mining methods using additional factors
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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 problem of traditional frequent pattern mining approach. While traditional approach may extract an enormous number of pattern results according to threshold settings, maximal frequent pattern mining approach mines a smaller number of representative 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 analyze their detailed characteristics.
KeywordsData mining Knowledge discovery Maximal frequent pattern Pattern mining Representative pattern
This study was funded by the Ministry of Education, Science and Technology of the National Research Foundation of Korea (NRF No. 20152062051 and NRF No. 20155054624).
Compliance with ethical standards
Conflicts of interest
Gangin Lee declares that he/she has no conflict of interest. Unil Yun declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors
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