一种改进的K-means聚类算法

An Improved K-means Clustering Algorithm

K-means算法是一种应用非常广泛的聚类算法,它有很多优点,比如操作简单、效率很高、伸缩性较好,但也存在一些不足,比如聚类个数需要人工输入、初始聚类中心随机产生可能导致局部最优解、孤立点对聚类结果会产生较大影响等。笔者主要针对K-means算法的K值获取和初始聚类中心的选取对算法进行改进,并通过实验对比了原算法和改进算法,实验表明改进算法在聚类准确率和质量方面都优于原算法。

The K-means algorithm is a very widely used clustering algorithm.It has many advantages,such as simple operation,high efficiency,good scalability,but there are some shortcomings,such as the number of clusters need manual input,the random clustering of the initial clustering center may lead to local optimal solution,and the isolated point will have a great influence on the clustering result.In this paper,the K-means algorithm is used to improve the K-means and the initial clustering center.The algorithm is compared with the original algorithm and the improved algorithm.The experimental results show that the improved algorithm is superior to the original algorithm.