基于MapReduce(EBC-MR)的进化双聚类方法

R. Rathipriya
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引用次数: 5

摘要

本文提出了一种新的双聚类方法,利用MapReduce框架将web数据、基因表达数据聚类到局部模式中。提出的双聚类方法使用一种称为平均相关值度量的相关度量来提取高度相干的双聚类。在此基础上,首次将基于MapReduce的遗传算法应用于web数据的双聚类。该方法可以避免大多数优化算法的局部收敛。利用MSWeb数据集和MSNBC网络使用数据集测试了基于MapReduce的进化分簇算法的性能。实验研究了该算法与传统遗传算法在双聚类中的比较。结果表明,该方法优于现有的进化双聚类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Evolutionary Biclustering Approach using MapReduce(EBC-MR)
A novel biclustering approach is proposed in this paper, which can be used to cluster data like web data, gene expression data into local pattern using MapReduce framework. The proposed biclustering approach extracts the highly coherent bicluster using a correlation measure called Average Correlation Value measure. Furthermore, MapReduce based genetic algorithm is firstly used to the biclustering of web data. This method can avoid local convergence in the optimization algorithms mostly. The MSWeb dataset and MSNBC web usage data set are used to test the performance of new MapReduce based Evolutionary biclustering algorithm. The experimental study is carried out for comparison of proposed algorithm with traditional genetic algorithm in biclustering. The results reveal that novel proposed approach preforms better than existing evolutionary biclustering approach.
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