Algorithms and Issues of Sequential Pattern Mining

The sequential pattern mining on progressive databases is very new approach, in which many researchersfind the sequential patterns. Period of interest is a sliding window continuously advancing as the time goes by. Sequential Web page Access pattern mining has been a focused theme in data mining research for over a decade with wide range of applications. The aim of discovering frequent sequential access (usage) patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. 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 presents the various works done on progressive sequential pattern mining. This paper classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at understanding of sequential pattern-mining problems, current status of provided solutions.