基于加权欧式距离的k_means算法研究

Research Based on Euclid Distance with Weights of K_means Algorithm

传统的k_means算法将欧式距离作为最常用的距离度量方法.针对基于欧式距离计算样本点与类间相似度的不足,用"相对距离"代替"绝对距离"可以更好地反映样本的实际分布,提出一种在领域知识未知的情况下基于加权欧式距离的k_means算法.针对公共数据库UCI里的数据实验表明改进后的算法能产生质量较高的聚类结果.

Euclid distance is commonly used to measure distance in the traditional k_means algorithm.The k_means algorithm based on weighted Euclid distance is researched and presented to overcome the existing problems of similarity calculation in clustering analysis based on traditional Euclid distance when we have no any domain knowledge about the data objects,the relative distance but not absolute distance is more accurately response to data distribution.Experiments on the standard database UCI show that the proposed method can produce a high accuracy clustering result.