Efficient Support Coupled Frequent Pattern Mining Over Progressive Databases

There have been many recent studies on sequential pattern mining. The sequential pattern mining onprogressive databases is relatively very new, in which we progressively discover the sequential patterns inperiod of interest. Period of interest is a sliding window continuously advancing as the time goes by. Asthe focus of sliding window changes , the new items are added to the dataset of interest and obsoleteitems are removed from it and become up to date. In general, the existing proposals do not fully explorethe real world scenario, such as items associated with support in data stream applications such as marketbasket analysis. Thus mining important knowledge from supported frequent items becomes a non trivialresearch issue. Our proposed novel approach efficiently mines frequent sequential pattern coupled withsupport using progressive mining tree.