关键词: K-Means, 聚类, 可变阈值, 初始聚类中心

Abstract: The K-Means algorithm selects the initial clustering centers randomly，which results in the performance of the clustering instability.In order to improve the limitation，a novel clustering algorithm（VTK-Means） based on variable threshold to select initial cluster centers is proposed in this paper.The algorithm tries to select the points whose distances to the existing initial points are longer than a threshold as the initial cluster centers，and then it appropriately adjusts the threshold according to the number of the points meeting the condition in the first step.The experimental results on UCI machine learning data sets indicate that it yields better stability compared with the typical K-means algorithm.

Key words: K-Means, clustering, variable threshold, initial cluster center