A sequential pattern mining using dynamic in stream environment

Abstract

Sequential pattern mining is the technique which finds out frequent patterns from the data set in time order. In this field, dynamic weighted sequential pattern mining is applied to a computing environment that changes according to the time, and it can be applied to a variety of environments applying changes of dynamic weight. In this paper, we propose a new sequence data mining method to discover frequent sequential patterns by applying the dynamic weight. This method reduces the number of candidate patterns by using the dynamic weight according to the relative time sequence. This method reduces the memory usage and processing time more than applying the existing methods dramatically. We show the importance of dynamic weighted mining through the comparison of existing weighted pattern mining techniques.