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
核
模糊K-均值
聚类
kernel
fuzzy K-means
clustering
