关键词: K-means, 初始中心, 离群点, 密度法, 最大最小距离

Abstract:

Both the random selection of initial clustering center and the empirical determination of [K] value have a certain impact on [K]-means clustering results. A [K]-means clustering algorithm based on weighted density and max-min distance is proposed. The clustering center set is selected by using the weighted density method to reduce the impact of outliers on clustering results. Then the center point is selected by the max-min distance to avoid the clustering result falling into local optimum. Finally, the value of [K] is determined by the ratio of the distance within clusters to the distance between clusters. Experiments show that the improved algorithm not only improves the accuracy of clustering, reduces the average iteration times of the algorithm, but also enhances the stability of the algorithm.

Key words: K-means, initial center, outliers, density method, max-min distance