Extracting Diverse Patterns with Unbalanced Concept Hierarchy
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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, in a given domain, a set of items can be grouped into a category and a pattern may contain the items which belong to multiple categories. In several applications, it may be useful to distinguish between the pattern having items belonging to multiple categories and the pattern having items belonging to one or a few categories. The notion of diversity captures the extent the items in the pattern belong to multiple categories. The items and the categories form a concept hierarchy. In the literature, an approach has been proposed to rank the patterns by considering the balanced concept hierarchy. In a real life scenario, the concept hierarchies are normally unbalanced. In this paper, we propose a general approach to calculate the rank based on the diversity, called drank, by considering the unbalanced concept hierarchy. The experiment results show that the patterns ordered based on drank are different from the patterns ordered based on support, and the proposed approach could assign the drank to different kinds of unbalanced patterns.
Keywordsdata mining association rules frequent patterns diversity diverse rank interestingness concept hierarchy algorithms
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