An efficient algorithm for mining periodic high-utility sequential patterns

摘要: A periodic high-utility sequential pattern (PHUSP) is a that not only yields (e.g. high profit) but also appears regularly in sequence database. Finding PHUSPs useful for several applications such as market basket analysis, where it can reveal recurring and profitable customer behavior. Although discovering desirable, computationally difficult. To discover efficiently, this paper proposes structure mining (PHUSPM) named PUSP. Furthermore, to reduce the search space speed up PHUSPM, pruning strategy developed. This results an efficient algorithm called optimal miner (PUSOM). An experimental evaluation was performed on both synthetic real-life datasets compare performance of PUSOM with state-of-the-art PHUSPM algorithms terms execution time, memory usage scalability. Experimental show efficiently complete set PHUSPs. Moreover, outperforms other four former prune many unpromising patterns using its designed strategy.