Probabilistic Static Load-Balancing of Parallel Mining of Frequent Sequences

PROJECT TITLE :

Probabilistic Static Load-Balancing of Parallel Mining of Frequent Sequences

ABSTRACT:

Frequent sequence mining is well-known and well studied downside in datamining. The output of the algorithm is used in several other areas like bioinformatics, chemistry, and market basket analysis. Unfortunately, the frequent sequence mining is computationally quite expensive. In this paper , we have a tendency to present a novel parallel algorithm for mining of frequent sequences primarily based on a static load-balancing. The static load-balancing is done by measuring the computational time employing a probabilistic algorithm. For affordable size of instance, the algorithms achieve speedups up to where is the number of processors. In the experimental evaluation, we show that our technique performs considerably higher then this state-of-the-art ways. The presented approach is very universal: it can be used for static load-balancing of different pattern mining algorithms like itemset/tree/graph mining algorithms.

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