基于粗糙集的支持向量聚类方法

针对支持向量聚类(Support Vector Clustering,SVC)的软核,引入了粗糙集的理论。通过粗糙集的上下边界,对SVC算法的核函数进行改进。通过聚类过程,算法不仅可以有效处理边界点,发现任意形状的软聚类集,还可以通过交互参数调整,控制结果的软边界。基于粗糙集的SVC解决了不确定边界的归类问题,而不需要额外的计算开销。实验结果表明算法能有效地处理软边界,同时也证实了算法的正确性。

Support vector clustering based on rough set

Rough set was applied to clustering method in view of soft kernel of support vector clustering(SVC).The kernel function was modified through introducing upper and lower boundary.During clustering,the algorithm can not only deal with boundary points and find soft clusters with arbitrary shapes,but also control softness of boundary region by interactively adjusting parameters.SVC based on rough set solved classification of uncertain boundary region without extra cost.The experimental results indicate that the method can deal with soft boundary effictively,proving its correctness.