CMRULES: An Efficient Algorithm for Mining Sequential Rules Common to Several Sequences

We propose CMRULES, an algorithm for mining sequential rules common to many sequences in sequence databases – not for mining rules appearing frequently in sequences. For this reason, the algorithm does not use a sliding-window approach. Instead, it first finds association rules to prune the search space for items that occur jointly in many sequences. Then it eliminates association rules that do not meet minimum confidence and support thresholds according to the time ordering. We evaluated the performance of CMRULES in three different ways. First, we provide an analysis of its time complexity. Second, we compared its performance on a public dataset with a variation of an algorithm from the literature. Results show that CMRULES is more efficient for low support thresholds, and has a better scalability. Lastly, we report a real application of the algorithm in a complex system.