Continuous Mining Approaches with Obsolete Database: A Review

Abstract

The sequential pattern mining on progressive databases is very incipient approach, in which many researchers progressively discover the patterns in period of interest. Period of interest is a sliding window perpetually advancing as the time goes by. As the focus of sliding window changes, the incipient items are integrated to the dataset of interest and obsolete items are abstracted from it and become au courant. In general, the subsisting proposals do not plenarily explore the authentic world scenario, such as items associated with support in data stream applications such as market basket analysis. Thus, mining consequential erudition from fortified frequent items becomes a nonpicayune research issue. This paper present the sundry works done on progressive sequential pattern mining. This paper presents a review of sequential pattern-mining techniques in the literature. This paper relegating sequential pattern-mining algorithms predicated on paramount key features fortified by the techniques. This relegation aims at understanding of sequential pattern mining quandaries, current status of provided solutions, and direction of research in this area. This paper additionally endeavors to provide a comparative performance analysis of many of the key techniques.