Comparative analysis of sequence weighting approaches for mining time-interval weighted sequential patterns

Unlike the general sequential pattern mining that considers only the generation order of data elements, mining weighted sequential patterns aims to get more interesting sequential patterns by considering the weights of data elements in a target sequence database in addition to their generation order. In general, for a sequence or a sequential pattern, not only the generation order of data elements but also their generation times and time-intervals are important because they can be helpful in finding more interesting sequential patterns. Applying the mining method of time-interval weighted sequential (TiWS) patterns that has been proposed in our previous work, this paper proposes several sequence weighting approaches to get the time-interval weight of a sequence in mining TiWS patterns for a sequence database, and the effectiveness of each approach in mining TiWS patterns is analyzed through a set of experiments. The proposed sequence weighting approaches may be helpful in obtaining more interesting sequential patterns in mining sequential patterns for a sequence database.