Mining Generalized Flow Patterns

Mining Generalized Flow Patterns

Wynne Hsu (National University of Singapore, Singapore), Mong Li Lee (National University of Singapore, Singapore) and Junmei Wang (National University of Singapore, Singapore)
Copyright: © 2008 |Pages: 20
DOI: 10.4018/978-1-59904-387-6.ch009
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Abstract

In this chapter, we investigate an efficient method to discover this class of relative-location sensitive flow patterns. These generalized flow patterns aim to summarize the sequential relationships between events that are prevalent in sharing the same topological structures. We adopt the pattern growth approach and develop an algorithm called GenSTMiner to discover these patterns. In order to increase the efficiency of the mining process, we also present two optimization techniques. The first is the use of conditional projected databases to prune infeasible events and sequences, and the second is pseudo projection to reduce memory requirement.

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