An Analytical Study on Sequential Pattern Mining With Progressive Database

The sequential pattern mining on progressive databases is very new approach, in which many researchers progressively discover the sequential patterns in period of interest. Period of interest is a sliding window continuously advancing as the time goes by. As the focus of sliding window changes, the new items are added to the dataset of interest and obsolete items are removed from it and become up to date. In general, the existing proposals do not fully explore the real world scenario, such as items associated with support in data stream applications such as market basket analysis. Thus mining important knowledge from supported frequent items becomes a non trivial research issue. This paper present the various works done on progressive sequential pattern mining .This paper presents a review of sequential pattern-mining techniques in the literature. This paper classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at understanding of sequential patternmining problems, current status of provided solutions, and direction of research in this area. This paper also tries to provide a comparative performance analysis of many of the key techniques