一种优化初始中心的K-means粗糙聚类算法

K-means rough clustering algorithm based on optimized initial center

针对K-means算法的不足,提出了一种优化初始中心的聚类算法。首先,采用密度敏感的相似性度量来计算对象的密度,基于对象之间的距离和对象的邻域,选择相互距离尽可能远的数据点作为初始聚类中心。然后,采用基于粗糙集的K-means聚类算法处理边界对象,同时利用均衡化函数自动生成聚类数目。实验表明,算法具有较好的聚类效果和综合性能。

For the shortage of K-means, a new K-means algorithm is proposed to optimize the initial center.Firstly, density-sensitive similarity measure is used to compute the density of objects.Based on the distance of objects and the neighborhood of object,the set of high density is obtained,and from which select data points whose mutual spearation is the great- est as possible as they can as initial centers.Then, a rough set-based K-means algorithm is uesd to deal with boundary region, and getting to the cluster number automatically by means of equalization funtion.Experimens show that the method has better cluster results and general performance.