  Now a days, data mining and knowledge discovery methods are applied to a
variety of enterprise and engineering disciplines to uncover interesting
patterns from databases. The study of Sequential patterns is an important data
mining problem due to its wide applications to real world time dependent
databases. Sequential patterns are inter-event patterns ordered over a
time-period associated with specific objects under study. Analysis and
discovery of frequent sequential patterns over a predetermined time-period are
interesting data mining results, and can aid in decision support in many
enterprise applications. The problem of sequential pattern mining poses
computational challenges as a long frequent sequence contains enormous number
of frequent subsequences. Also useful results depend on the right choice of
event window. In this paper, we have studied the problem of sequential pattern
mining through two perspectives, one the computational aspect of the problem
and the other is incorporation and adjustability of time constraint. We have
used Indiscernibility relation from theory of rough sets to partition the
search space of sequential patterns and have proposed a novel algorithm that
allows previsualization of patterns and allows adjustment of time constraint
prior to execution of mining task. The algorithm Rough Set Partitioning is at
least ten times faster than the naive time constraint based sequential pattern
mining algorithm GSP. Besides this an additional knowledge of time interval of
sequential patterns is also determined with the method.
