Improved k-means initial clustering center selection algorithm
Hao Zhi-qiang · Computer Engineering and Applications Journal · 2010
The traditional k-means has sensitivity to the initial clustering center.Considering this defection,a new improved algorithm is proposed.In the new algorithm,the density parameter of every data object is computed,and then k data objects with high density parameter are chosen as the initial clustering centers.Given the cluster number,and UCI database is used as testing datasets.The clustering results demonstrate that the improved algorithm can enhance the clustering stability and accuracy of ordinary k-means algorithm relatively.