Rough K-means clustering based on unbalanced degree of cluster
Tengfei Zhang, Chen Long, Li Yun · Kongzhi yu juece · 2013
Rough �� -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.