World Academic Industry Research Collaboration Organization (WAIRCO)
Sequential pattern mining is an important data mining problem with broad applications. Over the last decade many algorithms have been introduced. In this paper, a systematic survey of some of sequential pattern mining algorithms is performed by classifying into various categories. Most of the previously developed sequential pattern mining algorithms such as GSP, SPIRIT, and SPADE use Apriori-based approach by exploring a candidate generation-and-test approach, to reduce the number of candidate's examination. Then a comprehensive study on prefix-span algorithm, a projection-based sequential pattern-growth approach for efficient mining of sequential patterns is performed which shows that prefix-span, in most cases, outperforms the Apriori-based algorithm GSP, FreeSpan and SPADE.