Mining normalized weighted frequent sequential patterns with Time intervals algorithm

Abstract

In this paper, we propose a method for mining normalized weighted frequent sequential patterns with time intervals, we are not only interested in the number of occurrences of the sequence (the support), but also concerned about their level of different importance (weighted). We use the binding between the support and weight of the set range to candidates in mining normalized weighted frequent sequential patterns with time intervals while maintaining the downward closure property nature which allows a balance between support and the weight of a sequence.