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首页> 外文期刊>Journal of Cloud Computing: Advances, Systems and Applications >Efficient parallel spectral clustering algorithm design for large data sets under cloud computing environment
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Efficient parallel spectral clustering algorithm design for large data sets under cloud computing environment

机译：云计算环境下大数据集的高效并行频谱聚类算法设计

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摘要

Spectral clustering algorithm has proved be more effective than most traditional algorithms in finding clusters. However, its high computational complexity limits its effect in actual application. This paper combines the spectral clustering with MapReduce, through evaluation of sparse matrix eigenvalue and computation of distributed cluster, puts forward the improvement ideas and concrete realization, and thus improves the clustering speed of the distinctive clustering algorithm. According to the experiment, with the processing data scale being enlarged, the clustering rate is in nearly linear growth, and the proposed parallel spectral clustering algorithm is suitable for large data mining. The research results provide research basis to better design a clustering partition algorithm in large data and high efficiency.
机译：事实证明，谱聚类算法比大多数传统算法更有效地找到聚类。但是，其较高的计算复杂度限制了其在实际应用中的效果。本文将谱聚类与MapReduce相结合，通过稀疏矩阵特征值的估计和分布式聚类的计算，提出了改进思路和具体实现，从而提高了独特聚类算法的聚类速度。根据实验，随着处理数据规模的扩大，聚类速率几乎呈线性增长，提出的并行谱聚类算法适用于大数据挖掘。研究结果为更好地设计大数据，高效的聚类划分算法提供了研究依据。

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