一种基于Mahalanobis距离的增量聚类算法

摘要

经典的模糊c均值聚类算法对非球型或椭球型分布的数据集进行聚类效果较差.将经典的模糊c均值聚类中的欧氏距离用Mahalanobis距离替代,利用Mahalanobis距离的优点,将其用于增量学习中,提出一种基于马氏距离的模糊增量聚类学习算法.实验结果表明该算法能较有效地解决模糊聚类方法中的缺陷,提高了训练精度.%Classical fuzzy c-means clustering algorithm is inefficient to cluster non-spherical or elliptical distributed datasets. The paper replaces classical fuzzy c-means clustering Euclidean distance with Mahalanobis distance. It applies Mahalanobis distance to incremental learning for its merits. A Mahalanobis distance based fuzzy incremental clustering learning algorithm is proposed. Experimental results show the algorithm can not only effectively remedy the defect in fuzzy c-means algorithm but also increase training accuracy.