Abstract
We present a novel and powerful parallel algorithm for mining maximal frequent patterns, called Par-MinMax. It decomposes the search space by prefix-based equivalence classes, distributes work among the processors and selectively duplicates databases in such a way that each processor can compute the maximal frequent patterns independently. It utilizes multiple level backtrack pruning strategy and other novel pruning strategies, along with vertical database format, counting frequency by simple tid-list intersection operation. These techniques eliminate the need for synchronization, drastically cutting down the I/O overhead. The analysis and experimental results demonstrate the superb efficiency of our approach in comparison with the existing work.
This paper is supported by the National Natural Science Foundation of China under Grant No.60273075.
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Wang, H., Xiao, Z., Zhang, H., Jiang, S. (2003). Parallel Algorithm for Mining Maximal Frequent Patterns. In: Zhou, X., Xu, M., Jähnichen, S., Cao, J. (eds) Advanced Parallel Processing Technologies. APPT 2003. Lecture Notes in Computer Science, vol 2834. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39425-9_30
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DOI: https://doi.org/10.1007/978-3-540-39425-9_30
Publisher Name: Springer, Berlin, Heidelberg
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