基于初始中心点K均值聚类算法的改进方法研究

Research into Improved K-means Clustering Algorithm Based on Initial Center

传统聚类算法随机选取初始中心不能有效处理不规则数据集的边缘数据.该文主要叙述了K均值聚类算法基本思想和流程,详细分析了其算法的优点及存在的问题,提出对现有基于初始中心点K均值聚类算法的改进方法.

Traditional clustering algorithm randomly selected the initial center can not effectively deal with irregular data sets of edge data.The paper describes the basic idea and process of K-means clustering algorithm and analyzes its advantages and existing problems. The improved method of the present K-means clustering algorithm based on the initial center point is suggested.