关键词: K均值, 聚类中心, 样本密度, 全局优化

Abstract: Aiming at the problem of traditional K-means algorithm which is sensitive to initial clustering center and easy to fall into local optimum, this paper proposes a kind of global optimized K-means clustering algorithm based on sample density. In the iterative process, the KMS-GOSD algorithm obtains the pre-estimation density of the clustering center by Gaussian model, then the clustering center whose actual density is lower than the pre-estimated density at most will be dithered. The KMS-GOSD algorithm can not only overcome the dependence on the initial center of clustering, but also enhance the global exploration ability. It uses the standard UCI data sets as the contrast experiment objects, and finds that the improved algorithm has higher accuracy and stability compared with the traditional algorithm.

Key words: K-means, clustering center, sample density, global optimization