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There have been many recent studies on sequential pattern mining. Thesequential pattern mining on progressive databases is relatively very new, inwhich we progressively discover the sequential patterns in period of interest.Period of interest is a sliding window continuously advancing as the time goesby. As the focus of sliding window changes, the new items are added to thedataset of interest and obsolete items are removed from it and become up todate. In general, the existing proposals do not fully explore the real worldscenario, such as items associated with support in data stream applicationssuch as market basket analysis. Thus mining important knowledge from supportedfrequent items becomes a non trivial research issue. Our proposed novelapproach efficiently mines frequent sequential pattern coupled with supportusing progressive mining tree.

Efficient Support Coupled Frequent Pattern Mining Over Progressive Databases