Abstract
Previous studies have presented convincing arguments that a frequent sequence mining algorithm should not mine all frequent sequences but only the closed ones because the latter leads to not only more compact yet complete result set but also better efficiency. However, frequent closed sequence mining is still challenging on stand-alone for its large size and high dimension. In this paper, an algorithm, PFCSeq, is presented for mining frequent closed sequence based on distributed-memory parallel machine, in which each processor mines local frequent closed sequence set independently using task parallelism with data parallelism approach, and only two communications are needed except that imbalance is detected. Therefore, time spent in communications is significantly reduced. In order to ensure good load balance among processors, a dynamic workload balance strategy is proposed. Experiments show that it is linearly scalable in terms of database size and the number of processors.
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Ma, C., Li, Q. (2005). Parallel Algorithm for Mining Frequent Closed Sequences. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds) Autonomous Intelligent Systems: Agents and Data Mining. AIS-ADM 2005. Lecture Notes in Computer Science(), vol 3505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492870_15
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DOI: https://doi.org/10.1007/11492870_15
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