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An initialization method based on the core clusters for locality-weight fuzzy c-means clustering

机译:基于核心聚类的局部权重模糊c均值聚类初始化方法

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The Locality-weight fuzzy c-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality-weight fuzzy c-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality-weight fuzzy c-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality-weight fuzzy c-means clustering.
机译:最近提出了局部权重模糊c均值聚类方法。尽管这种方法可以提高聚类的准确性,但是由于初始中心使用了一些随机样本,因此通常会获得不稳定的聚类结果。本文将基于核聚类的初始化方法用于局部权重模糊c均值聚类。核心聚类可以通过构建σ邻域图来形成,其中心被视为局部权重模糊c均值聚类的初始中心。为了研究我们方法的有效性,在三个数据集上进行了几次实验。实验结果表明,与以前的局部权重模糊c均值聚类算法相比,本文提出的方法可以提高聚类性能。

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