Research Report MINING SEQUENTIAL PATTERNS: GENERALIZATIONS AND PERFORMANCE IMPROVEMENTS

The problem of mining sequential patterns was recently introduced in [AS95]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-speci ed minimum support, where the support of a pattern is the number of data-sequences that contain the pattern. An example of a sequential pattern is \5% of customers bought `Foundation' and `Ringworld' in one transaction, followed by `Second Foundation' in a later transaction". We generalize the problem as follows. First, we add time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern. Second, we relax the restriction that the items in an element of a sequential pattern must come from the same transaction, instead allowing the items to be present in a set of transactions whose transaction-times are within a user-speci ed time window. Third, given a user-de ned taxonomy (is-a hierarchy) on items, we allow sequential patterns to include items across all levels of the taxonomy. We present GSP, a new algorithm that discovers these generalized sequential patterns. Empirical evaluation using synthetic and real-life data indicates that GSP is much faster than the AprioriAll algorithm presented in [AS95]. GSP scales linearly with the number of data-sequences, and has very good scale-up properties with respect to the average datasequence size. Also, Department of Computer Science, University of Wisconsin, Madison.