A Clustering Algorithm Based on Variance-Similarity

被引：0

作者：

Li, Zhendong ^{[1
]}

Li, Fei ^{[2
]}

机构：

[1] Lanzhou Univ Finance & Econ, Sch Informat Engn, Lanzhou, Peoples R China

[2] Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou, Peoples R China

来源：

MEASUREMENT TECHNOLOGY AND ENGINEERING RESEARCHES IN INDUSTRY, PTS 1-3 | 2013年 / 333-335卷

关键词：

data mining; clustering algorithm; attribute values; Variance-Similarity;

D O I：

10.4028/www.scientific.net/AMM.333-335.1306

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Clustering algorithms, like K-means Algorithm, use distances in attribute space to cluster data. However the computation of distances in attribute space influences the accuracy. So innovatively, Variance-Similarity clustering algorithm defines similarity as a function of the attribute variance, and clusters data by the comparison of similarities. In computer simulation, the comparison of Variance-Similarity Algorithm and K-means Algorithm on UCI data sets presents that Variance-Similarity Algorithm has a better clustering accuracy than K-means Algorithm.

引用

页码：1306 / +

页数：2