Discovering diverse-frequent patterns in transactional databases

Abstract

In the area of data mining, the process of frequent pattern extraction finds interesting information about the association among the items in a transactional database. The notion of support is employed to extract the frequent patterns. Normally, a frequent pattern may contain items which belong to different categories of a particular domain. The existing approaches do not consider the notion of diversity while extracting the frequent patterns. For certain types of applications, it may be useful to distinguish between the frequent patterns with items belonging to different categories and the frequent patterns with items belonging to the same category. In this paper we propose a new interestingness measure, called DiverseRank, to rank the frequent patterns based on the items' categories. Given a set of frequent patterns, we propose an efficient algorithm to extract the diverse-frequent patterns. Experiments on the real-world data set show that the diverse-frequent patterns extracted with the proposed DiverseRank measure are different from the frequent patterns extracted with the support measure.