Mining multi-attribute event sequential pattern based on association rule

The traditional sequential pattern algorithms often have some limitations in solving the applications. First, ignore the time-interval trait of the event sequences, which is more important to the mining result. Secondly, the sequential item is also without attribute constraint. As a result, in this paper an idea is presented to mine event sequential pattern with multi-attribute constraint. Based on the algorithm of AprioriAll and Apriori, the transition time is taken into account between events. According to the layer idea, the key task is to mine the frequent sequential pattern first, then to find out the association rules in the attribute constraint items. In the end provides a way to mine sequential pattern with multi-attribute constraint by example analysis.