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Optimal parallel clustering algorithms on a reconfigurable array of processors with wider bus networks

机译:具有更宽总线网络的可重配置处理器阵列上的最佳并行集群算法

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Clustering techniques are usually used in pattern recognition, image segmentation and object detection. For N patterns and k centers each with M features, in this paper, we first design an O(kM) time optimal parallel algorithm for one pass process of clustering with the k-menas method on a linear array of processors with a wider bus network using N~1+1/ε processors with one bus network, where c is any constant and c >=1. Then, based on the proposed algorithm, two O(k) and O(1) time optimal parallel clustering algorithms are also derived using MN~1+1/ε and kMN1+1/ε processors with M row row and MN row bus networks, respectively. These results improve the best known bounds and achieve cost optimal in their time and processor complexities.
机译:聚类技术通常用于模式识别,图像分割和目标检测。对于N个模式和k个中心,每个中心具有M个特征,在本文中,我们首先设计了一种O(kM)时间最优并行算法,用于通过k-menas方法在具有较宽总线网络的线性处理器阵列上进行一次遍历聚类过程。使用具有一个总线网络的N〜1 + 1 /ε处理器,其中c为任意常数,c> = 1。然后,基于所提出的算法,还分别使用具有M行行和MN行总线网络的MN〜1 + 1 /ε和kMN1 + 1 /ε处理器,导出了两种O(k)和O(1)时间最优并行聚类算法。这些结果改善了最著名的界限,并在时间和处理器复杂性方面实现了最佳成本。

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