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Extracting Diverse Patterns with Unbalanced Concept Hierarchy

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)

Abstract

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.

Keywords

data mining association rules frequent patterns diversity diverse rank interestingness concept hierarchy algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Centre of Data EngineeringInternational Institute of Information Technology-Hyderabad (IIIT-H)HyderabadIndia

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