An integrated sequential patterns mining with fuzzy time-intervals

Abstract

One important issue in the sequential pattern mining is to discover frequent sequential patterns in a sequence database. The order of times is the focus of the previous works. However, there is seldom discussion on the time interval between successive items in patterns before. With the time interval to make decision, sequential pattern is better than which with the order of items. In this paper, we propose an algorithm called integrated sequential pattern mining with fuzzy time intervals (ISPFTI). The main idea of ISPFTI algorithm is to use the a priori-like method to mine the frequent sequential patterns of sequence database and use fuzzy theory to mine the time interval between frequent sequences. Firstly, find the candidate sequential patterns. Then, the frequent sequential patterns are found with the minimum fuzzy support. In the step of finding frequent sequential patterns, use the fuzzy number to find each time cluster by computing its fuzzy support. And the results are the frequent fuzzy time sequential patterns. Finally, the experimental result verifies that result of our proposed ISPFTI algorithm performs the excellence of which only with the fuzzy sequential patterns mining or fixed time interval.