Kernel method-based fuzzy clustering algorithm
Kernel method-based fuzzy clustering algorithm
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发表时间：
2005
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通讯作者：
WuZhongdong;GaoXinbo;XieWeixin;YuJianping
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作者：
WuZhongdong;GaoXinbo;XieWeixin;YuJianping
将模糊C-均值聚类算法（FCM）扩展为模糊核C-均值聚类算法（FKCM），以有效地对非超球面数据、含噪声数据、异质聚类原型混合数据、非对称数据等进行聚类分析。对合成数据和真实的数据的实验结果表明，与FCM算法相比，FKCM聚类算法具有通用性，能够有效地对具有不同结构的数据集进行无监督分析。可以想见，基于核的聚类算法是模糊聚类分析的重要研究方向之一。
The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.