VRKSHA: A Novel Multi-Tree Based Sequential Approach for Seasonal Pattern Mining

Abstract

Mining association patterns from a time-stamped temporal database is implicitly associated with task of scanning input database. Finding supports of itemsets requires scanning the input database. Database scan can be either snapshot or lattice based. Sequential method for similarity profiled association pattern mining originally proposed by Jin Soung Yoo and Sashi Sekhar is based on the snapshot database scan. Snapshot database scan involves scanning multi-time slot database, time slot by time slot. The major limitation of sequential method is the requirement to retain original temporal database in the memory for finding itemset supports. In this paper, a novel multi-tree structure called VRKSHA is proposed that eliminates the need to store the original temporal database in memory. The basic idea is to generate a time stamped temporal tree and use this multi-tree structure to obtain true supports of temporal itemsets for a given time slot. Discovery of similar temporal itemsets is based on finding distance between temporal itemset and reference and validating if the computed distance satisfies specified user dissimilarity threshold. A pattern is pruned if the dissimilarity condition fails at any given time slot well before computing true support of itemset w.r.t all time slots. The advantage of proposed sequential approach is from the fact that it does not require database retention in memory. The case study demonstrating working example proves the significance and efficiency of the proposed approach.