基于最小包含球的大数据集快速谱聚类算法

 GRC (Graph-based Relaxed Clustering)是一种具有便捷性和自适应性的谱聚类算法,但对于大数据集,繁重的时间开销限制了其实用性.针对此不足,该文通过对GRC聚类指示向量进行约束并融合中心约束型最小包含球(Center-Constrained Minimal Enclosing Ball,CCMEB)理论提出了大数据集快速谱聚类算法CCMEB-CGRC.该算法继承GRC的便捷性和自适应性的同时又具有渐近线性时间复杂度的优点,从而较好地解决了大数据集快速有效谱聚类的问题.仿真实验的结果验证了该算法的有效性和快速性.

Fast Spectral Clustering for Large Data Sets Using Minimal Enclosing Ball

Graph-based relaxed clustering (GRC) is a spectral clustering algorithm with straightforwardness and adaptability.However,for large data sets,its heavy time cost severely weakens its usefulness.In order to overcome this shortcoming,a novel algorithm named CCMEB-based constrained GRC (CCMEB-CGRC) is proposed in this study by constraining the clustering indicator of GRC and introducing Center-Constrained Minimal Enclosing Ball (CCMEB).This algorithm has the merit of asymptotic linear time complexity as well as inherits the straightforwardness and adaptability of GRC.Thus,a fast and efficient spectral clustering method fitting for large data sets is proposed.This is confirmed in the experimental studies.