Sequential Patterns Mining with Fuzzy Time-Intervals

The task of sequential pattern mining is useful for various applications, including market analysis, decision support, and business management. One important issue is to discover frequent sequential patterns in a sequence database. And most of the previous works have focus on the order of times. However, the time interval between successive items in patterns is seldom discussed before. With the order of items, sequential pattern is not as good as which is extended with time interval to make the decision. In this paper, we propose an algorithm called sequential pattern mining with fuzzy time intervals (SPFTI). The main idea of SPFTI algorithm is to use the Apriori-like method to mine the frequent sequential patterns of sequence database and use fuzzy theory to mine the time interval between frequent sequences. At first, find the candidate sequential patterns. Then, the frequent sequential patterns are found with the minimum 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 SPFTI algorithm outperforms with the fuzzy sequential patterns mining with fixed time interval.