This paper addresses the problem of big association rule mining using an evolutionary approach. The mimetic method has been successfully applied to small and medium size databases. However, when applied on larger data...
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ISBN:
(纸本)9789811064876;9789811064869
This paper addresses the problem of big association rule mining using an evolutionary approach. The mimetic method has been successfully applied to small and medium size databases. However, when applied on larger databases, the performance of this method becomes an important issue and current algorithms have very long execution times. Modern CPU/GPU architectures are composed of many cores, which are massively threaded and provide a large amount of computing power, suitable for improving the performance of optimization techniques. The parallelization of such method on GPU architecture is thus promising to deal with very large datasets in real time. In this paper, an approach is proposed where the rule evaluation process is parallelized on GPU, while the generation of rules is performed on a multi-core CPU. Furthermore, an intelligent strategy is proposed to partition the search space of rules in several independent sub-spaces to allow multiple CPU cores to explore the search space efficiently and without performing redundant work. Experimental results reveal that the suggested approach outperforms the sequential version by up to at 600 times for large datasets. Moreover, it outperforms the-state-of-the-art high performance computing based approaches when dealing with the big WebDocs dataset.
Study of algorithms and its design can be progressed in various dimensions. In this paper, we have a definite refinement of lower bound on the number of tracks required to route a channel. The attack is from a complem...
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ISBN:
(纸本)0387446397
Study of algorithms and its design can be progressed in various dimensions. In this paper, we have a definite refinement of lower bound on the number of tracks required to route a channel. The attack is from a complementary viewpoint. Our algorithm succeeds to avoid all kind of approximation. The approach performs exact mapping of the problem into graphical presentation and analyzes the graph taking help of mimetic algorithm, which uses combination of sequential and GA based vertex coloring. Performance of the algorithm depends on how effectively mimetic approach can applied selecting appropriate Values for the parameters to evaluate the graphical presentation of the problem. This viewpoint has immense contribution against sticking at local minima for this optimization problem. The finer result clearly exemplifies instances, which give better or at least the same lower bound in VLSI channel routing problem.
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