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- W2326132979 abstract "Frequent sequence mining is well known and well studied problem in datamining. The output of the algorithm is used in many other areas like bioinformatics, chemistry, and market basket analysis. Unfortunately, the frequent sequence mining is computationally quite expensive. In this paper, we present a novel parallel algorithm for mining of frequent sequences based on a static load-balancing. The static load-balancing is done by measuring the computational time using a probabilistic algorithm. For reasonable size of instance, the algorithms achieve speedups up to <inline-formula><tex-math notation=LaTeX>$approx 3/4cdot P$</tex-math></inline-formula> where <inline-formula><tex-math notation=LaTeX> $P$</tex-math> </inline-formula> is the number of processors. In the experimental evaluation, we show that our method performs significantly better then the current state-of-the-art methods. The presented approach is very universal: it can be used for static load-balancing of other pattern mining algorithms such as itemset/tree/graph mining algorithms." @default.
- W2326132979 created "2016-06-24" @default.
- W2326132979 creator A5059998807 @default.
- W2326132979 date "2016-05-01" @default.
- W2326132979 modified "2023-10-17" @default.
- W2326132979 title "Probabilistic Static Load-Balancing of Parallel Mining of Frequent Sequences" @default.
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- W2326132979 doi "https://doi.org/10.1109/tkde.2016.2515622" @default.
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