Parallel Algorithms for Sequence Mining

Valerie Guralnik, Nivea Garg, and George Karypis
Euro-Par 2001: 310-320, 2001
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Discovery of sequential patterns is becoming increasingly useful and essential in many scientific and commercial domains. Enormous sizes of available datasets and possibly large number of mined patterns demand efficient and scalable algorithms. In this paper we present two parallel formulations of a serial sequential pattern discovery algorithm based on tree projection that are well suited for distributed memory parallel computers. Our experimental evaluation on a 32 processor IBM SP show that these algorithms are capable of achieving good speedups, substantially reducing the amount of the required work to find sequential patterns in large databases.
Research topics: Data mining | Parallel processing | Pattern discovery