An enhanced k-means algorithm using agglomerative hierarchical clustering strategy
Jianjun Cheng, Xiaoyun Chen, Haijuan Yang, Mingwei Leng · 2012
To overcome the drawback that the k-means algorithm is sensitive to the selection of initial centroids, we proposed an enhanced two-stage k-means algorithm. In the first stage, we begin with selecting as many as enough initial centroids, then the basic k-means algorithm is applied to get the intermediate clusters, i.e., we keep the number of initial centroids k′ large enough to eliminate the bad centroids' effect to the result. In the second stage, the k′ intermediate clusters are merged into k result clusters using agglomerative hierarchical clustering algorithm. We have tested our algorithm on standard data sets and synthesized data set; experiments results have manifested that our algorithm can obtain higher clustering accuracy.