An efficient clustering algorithm
Yufang Zhang, Jia-Li Mao, Zhongyang Xiong · 2004
Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. As the dataset's scale increases rapidly, it is difficult to use K-means and deal with massive data. An improved K-means algorithm is presented. It can avoid getting into locally optimal solution in some degree, and reduce the probability of dividing one big cluster into two or more ones owing to the adoption of squared-error criterion. The experiments demonstrate that the improved K-means is more stable and more accurate.