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Parallelizing the Unsupervised k-Windows Clustering Algorithm

机译:并行化无监督的K-Windows聚类算法

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摘要

Clustering can be defined as the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups, called clusters. The growing need for parallel clustering algorithms is attributed to the huge size of databases that is common nowadays. This paper presents a parallel version of a recently proposed algorithm that has the ability to scale very well in parallel environments mainly regarding space requirements but also gaining a time speedup.
机译:群集可以定义为将一组模式分成差异和同质的有意义的组,称为群集的过程。不断增长的并行聚类算法归因于现在是常见的巨大尺寸。本文介绍了最近提出的算法的并行版本,其能够在主要关于空间需求的并行环境中展示非常好,但也获得时间加速。

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