摘要
在模糊k平面聚类(KPC)算法的基础上,通过引入正交约束提出正交模糊k平面聚类算法(OFKPC).与KPC及模糊KPC(FKPC)类似,OFKPC仍从原型出发,用k组超平面替代传统的点(类中心)作为聚类原型.同时根据KPC及FKPC的思想,中心超平面是用来尽量区分不同类样本,因此这些超平面法向量构成的矩阵可用来进行特征降维.在人工数据集和UCI数据集上实验表明,OFKPC算法不仅较FKPC算法有更好的聚类效果,且具有更强的特征降维能力.
A clustering algorithm named Orthogonal Fuzzy k-Plane Clustering （OFKPC） is presented by introducing orthogonal restriction into Fuzzy k-Plane Clustering （FKPC）. Similar to KPC and FKPC, OFKPC still uses k group hyperplanes as the prototypes of cluster centers. According to the idea of KPC and FKPC, the hyperplanes are built to distinguish samples in different classes. So the matrices constructed by the normal vectors of these hyperplane,s can be used to reduce dimensionality. Experimental results on both artificial and UCI datasets show that OFKPC not only has better clustering results than FKPC but also has the ability of reducing dimensionality.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2011年第6期783-791,共9页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60903100
60975027)
江苏省普通高校研究生科研创新计划项目(No.CXZZ11_0483)资助
