关键词: K-Means, 聚类算法, 聚类中心, 离群点

Abstract: The K-Means algorithm is a partition-based algorithm in cluster analysis. With an unsupervised learning algorithm, its advantages of simple thinking, good effect and easy implementation are widely used in fields such as machine learning. But the K-Means algorithm also has certain limitations. For example, the K number of clusters in the algorithm is difficult to determine how to choose the initial cluster center, how to detect and remove outliers and the distance and similarity measure. This paper summarizes the improvement of K-Means algorithm from several aspects, and compares it with the classical K-Means algorithm. In addition, it analyzes the advantages and disadvantages of the improved algorithm, and points out the problems. Finally, the development direction and trend of K-Means algorithm are prospected.

Key words: K-Means, clustering algorithm, cluster center, outliers