  There have been many recent studies on sequential pattern mining. The
sequential pattern mining on progressive databases is relatively very new, in
which we 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. Our proposed novel
approach efficiently mines frequent sequential pattern coupled with support
using progressive mining tree.
