Natural Exponent Inertia Weight-based Particle Swarm Optimization for Mining Serial Episode Rules from Event Sequences

An episode rule mining to extract useful and important patterns or episodes from large event sequences represents the temporal implication of associating the antecedent and consequent episodes. The existing technique for mining precise-positioning episode rules from event sequences, mines serial episodes resulting in enormous memory consumption. To resolve this issue, the proposed work ensures the generation of fixed-gap episodes and parameter settings through the use of Particle Swarm Optimization mechanism. Fixed-gap episodes are generated using Natural Exponent Inertia Weight-based Particle Swarm Optimization algorithm. In this paper, a new technique called Mining Serial Episode Rules (MSER) is proposed, which utilizes the correlation between episodes and the generation of parameter selection where the occurrence time of an event is specified in the consequent. Further, a trie-based data structure to mine MSER along with a pruning technique is incorporated in the proposed methodology to improve the performance. The efficiency of the proposed algorithm MSER is evaluated on three benchmark data sets Retail, Kosarak, and MSNBC where the experimental results outperform the existing methods with respect to memory usage and execution time.