An Asynchronous Periodic Sequential Patterns Mining Algorithm with Multiple Minimum Item Supports

Abstract

Original sequential pattern mining model only considers occurrence frequentness of sequential patterns, disregards their occurrence periodicity. We propose the asynchronous periodic sequential pattern mining model to discover the sequential patterns which are not only occurring frequently, but also appearing periodically. For this mining model, we propose a pattern-growth mining algorithm to mine asynchronous periodic sequential patterns with multiple minimum item supports. This algorithm employs a dividing and rule method to mine asynchronous periodic sequential pattern recursively and depth first. Experimental results show the efficiency and stability of the algorithm.