Mining popular patterns from transactional databases

Abstract

Since the introduction of the frequent pattern mining problem, researchers have extended frequent patterns to different useful patterns such as cyclic, emerging, periodic and regular patterns. In this paper, we introduce popular patterns, which captures the popularity of individuals, items, or events among their peers or groups. Moreover, we also propose (i) the Pop-tree structure to capture the essential information for the mining of popular patterns and (ii) the Pop-growth algorithm for mining popular patterns. Experimental results showed that our proposed tree structure is compact and space efficient and our proposed algorithm is time efficient. © 2012 Springer-Verlag.