[1]张忠林,曹志宇,李元韬..基于加权欧式距离的k_means算法研究[J].郑州大学学报(工学版),2010,31(01):92.
　ZHANG Zhonglin,CAO Zhiyu,LI Yuantao.Research Based on Euclid Distance with Weights of K——means Algorithm[J].Journal of Zhengzhou University (Engineering Science),2010,31(01):92.
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基于加权欧式距离的k_means算法研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
31
期数:
2010年01期
页码:
92
栏目:
出版日期:
2010-01-30

文章信息/Info

Title:
Research Based on Euclid Distance with Weights of K——means Algorithm
作者:
张忠林; 曹志宇; 李元韬.
兰州交通大学电子与信息工程学院,甘肃,兰州,730070
Author(s):
ZHANG Zhonglin; CAO Zhiyu; LI Yuantao
School of Electronics and Information Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China
Keywords:
k_means algorithm; clustering; weight; coefficient of variation
文献标志码:
A
摘要:
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.
Abstract:
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 anydomain knowledge about the data objects，the relative distance but not absolute distance is more accurately re—sponse to data distribution．Experiments on the standard database UCI show that the proposed method can produce ahigh accuracy clustering result．
更新日期/Last Update: 1900-01-01