基于簇内不平衡度量的粗糙K-means聚类算法

Rough K-means clustering based on unbalanced degree of cluster

粗糙K-means聚类算法是一种有效的处理聚类边界模糊问题的算法,但大多数算法对簇的下近似集和边界中的对象使用统一的权值,忽略了簇内对象之间的差异性.针对这一问题提出一种新的改进算法,通过对簇内的每个对象加入簇内不平衡度量,以区分不同对象对簇的贡献程度,使得聚类结果簇内更紧凑、簇间更疏远.不同数据集的仿真实验结果表明,所提出算法可以有效提高聚类结果的精度.

Rough K-means clustering is a valid algorithm to process the inseparability of border of clusters. But to most algorithms, weights of objects in the lower approximate set or the upper approximate set are all the same without paying attention to the diversity in clusters. Therefore, a new algorithm is proposed. The algorithm can make the cluster has a more compact ＂center, and the borders are separated each other with the unbalanced degree of cluster which means the contribution of an object to the cluster, The simulation analysis shows that this algorithm can improve the precision of the clustering results effectively.