基于样本协方差矩阵迹的聚类算法

A Clustering Algorithm Based on the Trace of Sample Covariance Matrix

针对传统聚类算法在处理某些非球形分布数据的不足,提出了一种基于样本协方差矩阵迹的聚类算法.该算法由数据集归一化、初始类别构造和初始类别二次融合这三个主要步骤构成.仿真结果表明,与传统的 FCA相比,本文算法在无需聚类数目的情况下,处理某些非球形分布数据集时具有更好的聚类效果.

Aiming at the shortage of traditional clustering algorithm when dealing data with some non- spherical-shape distribution, a novel clustering algorithm based on the trace of sample covariance matrix is presented in this paper. This algorithm is made up of the three main parts-uniform for data, constitution of initial patterns and fusion of initial patterns. The simulation results show that compared with the traditional FCA, the proposed algorithm has good clustering performance for data with some non-spherical-shape distribution without the number of clustering.