An Efficient Algorithm for Mining Sequential Rules with Interestingness Measures

Mining sequential rules are an important problem in data mining research. It is commonly used for market decisions, management and behaviour analysis. In traditional association-rule mining, rule interestingness measures such as confidence are used for determining relevant knowledge. They can reduce the size of the search space and select useful or interesting rules from the set of the discovered ones. Many studies have examined the interestingness measures for mining association rules, but have not been devoted to mine sequential rules in sequence databases. In this paper, we thus consider and apply several interestingness measures to generate all relevant sequential rules from a sequence database. The prefix tree structure is also used to get the support values of sequential patterns faster and reduce the execution time for mining sequential rules. Our experimental results show that the run time for mining sequential rules with interestingness measures on the prefix tree structure is much faster than that of other algorithms.