A multi-supports-based sequential pattern mining algorithm

Sequential pattern mining is now widely used in various areas, such as the analysis of biological sequences, Web access patterns, customer purchase patterns and etc. In this paper, we propose a new definition for M-sequences. Also we present multiple supports: local support, total support, and distribution support for their related mining of local sequential patterns, total sequential patterns and existence sequential patterns. Based on multiple supports, a multi-supports-based sequential pattern mining algorithm is developed which can be generally applied to find such patterns