Mining Time-Interval Sequential Patterns Using Clustering Analysis

Abstract

A time-interval sequential pattern is a sequential pattern with the information about the time intervals between itemsets. Most algorithms of mining time-interval sequential patterns find the time intervals between itemsets by predefining some non-overlap time partitions, however, a predefined set of non-overlap time partitions cannot be suitable for every pair of successive itemsets. Therefore, in this paper, we present a new algorithm to mine time-interval sequential patterns without defining any time partitions in advance. The algorithm first adopts the clustering analysis to automatically generate the suitable time partitions for frequent occurring pairs of successive itemsets, and then uses these generated time partitions to extend typical algorithms to discover the time-interval sequential patterns. Our result of experiment verifies that this algorithm outperforms than the algorithms which use some predefined and non-overlap time partitions.