Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences

Published by Data Mining and Knowledge Discovery on April 14, 2004


Ben Kao, Minghua Zhang, Chi Lap Yip, David W. Cheung, Usama M. Fayyad

Data Mining and Knowledge Discovery volume 10, pages87–116 (2005)

Abstract. We study two problems: (1) mining frequent sequences from a transactional database, and (2) incremental update of frequent sequences when the underlying database changes over time. We review existing sequence mining algorithms including GSP, PrefixSpan, SPADE, and ISM. We point out the large memory requirement of PrefixSpan, SPADE, and ISM, and evaluate the performance of GSP. We discuss the high I/O cost of GSP, particularly when the database contains long frequent sequences. To reduce the I/O requirement, we pro-pose an algorithm MFS, which could be considered as a generalization of GSP. The general strategy of MFS is to first find an approximate solution to the set of frequent sequences and then perform successive refinement until the exact set of frequent sequences is obtained. We show that this successive refinement approach results in a significant improvement in I/O cost. We discuss how MFS can be applied to the incremental update problem. In particular, the result of a previous mining exercise can be used (by MFS) as a good initial approximate solution for the mining of an updated database. This results in an I/O efficient algorithm. To improve processing efficiency, we devise pruning techniques that, when coupled with GSP or MFS, result in algorithms that are both CPU and I/O efficient.

Keywords: data mining, sequence, incremental update.

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