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基于核的非凸数据模糊K-均值聚类研究 被引量:7

Fuzzy K-means clustering algorithm to non-spherical shape data based on kernel
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摘要 将模糊K-均值聚类算法与核函数相结合,采用基于核的模糊K-均值聚类算法来进行聚类。核函数隐含地定义了一个非线性变换,将数据非线性映射到高维特征空间来增加数据的可分性。该算法能够解决模糊K-均值聚类算法对于非凸形状数据不能正确聚类的问题。 Fuzzy K-means clustering algorithm is introduced, which is based on kernel by integrating fuzzy K-means clustering algorithm with kernel. Kernel function implicitly defines a non-linear transformation that maps the data from their original space to a high dimensional feature space to increase the separability of data. This algorithm can solve the problem that the fuzzy K-means clustering algorithm cannot correctly cluster the non-soherical shape data.
出处 《计算机工程与设计》 CSCD 北大核心 2005年第7期1784-1785,1792,共3页 Computer Engineering and Design
关键词 模糊K-均值 聚类 kernel fuzzy K-means clustering
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参考文献7

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