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A novel two-stage algorithm of Fuzzy C-Means clustering

机译:一种新颖的两阶段模糊C-均值聚类算法

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Fuzzy C-Means is one of the clustering algorithms based on optimizing an objective function. The selection of the initial parameters of the number and the initial cluster centers play an important influence in the performance of the FCM. This paper proposes a new FCM clustering algorithm with two stages. The proposed algorithm not only resolves the problem of the initial choice of the cluster center effectively, but also decreases the time when clustering large volume of data. The computer simulation results show the effectivity and the superiority of the new algorithm.
机译:模糊C均值是基于优化目标函数的聚类算法之一。数量的初始参数和初始聚类中心的选择对FCM的性能起着重要的影响。本文提出了一种新的具有两个阶段的FCM聚类算法。该算法不仅有效地解决了聚类中心的初始选择问题,而且减少了聚类大量数据的时间。计算机仿真结果表明了该算法的有效性和优越性。

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