A data mining approach to discovering reliable sequential patterns

• We propose a new sequential pattern mining approach: P-PrefixSpan.
• This approach imposes minimum time-probability constraint.
• The discovered sequential pattern can yield information not only of the order of frequent items but also of the time probability of arrival items.
• P-PrefixSpan is compared with PrefixSpan in terms of number of patterns obtained and execution efficiency.

Sequential pattern mining is a data mining method for obtaining frequent sequential patterns in a sequential database. Conventional sequence data mining methods could be divided into two categories: Apriori-like methods and pattern growth methods. In a sequential pattern, probability of time between two adjacent events could provide valuable information for decision-makers. As far as we know, there has been no methodology developed to extract this probability in the sequential pattern mining process. We extend the PrefixSpan algorithm and propose a new sequential pattern mining approach: P-PrefixSpan. Besides minimum support-count constraint, this approach imposes minimum time-probability constraint, so that fewer but more reliable patterns will be obtained. P-PrefixSpan is compared with PrefixSpan in terms of number of patterns obtained and execution efficiency. Our experimental results show that P-PrefixSpan is an efficient and scalable method for sequential pattern mining.