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Sequential patterns in customer transactional databases are commonly mined for E-Commerce recommendations. In many practical applications, the absence of certain item-sets and sequences could have important implications. Mining frequent sequences comprising not only the occurrence but also the absence of certain sequences will increase the accuracy of product recommendations. A sequential pattern containing at least one absent itemset is called a negative sequential pattern. In this paper, we formulate the problem of negative sequential pattern mining by introducing practical constraints and propose an algorithm called PNSP for the mining. The discovered patterns can then be more interesting and effective to use. The experimental results show that PNSP may discover negative sequential patterns for practical E-commerce applications.

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Common sequential pattern mining algorithms handle static databases. Once the data change, the previous mining result will be incorrect, and we need to restart the entire mining process for the new updated sequence database. Previous approaches, within either Apriori-based or projection-based framework, mine patterns in a forward manner. Considering the incremental characteristics of sequence-merging, we develop a novel technique, called backward mining, for efficient incremental pattern discovery. We propose an algorithm, called BSPinc, for incremental mining of sequential patterns using a backward mining strategy. Stable sequences, whose support counts remain unchanged in the updated database, are identified and eliminated from the support counting process. Candidate sequences generated using backward extensions can be mined recursively within the ever-shrinking space of the projected sequences. The experimental results show that BSPinc worked an average of 2.5 times faster than the well-known IncSpan algorithm and outperformed SPAM an average of 3 times faster. Keywords-incremental discovery, sequential pattern, backward mining I. 2009 International Conference on Computational Science and Engineering 978-0-7695-3823-5/09 $26.00