[1]王晓阳,张洪渊,沈良忠,等.基于相似性度量的高维数据聚类算法研究[J].计算机技术与发展,2013,(05):30-33.
　WANG Xiao-yang,ZHANG Hong-yuan,SHEN Liang-zhong,et al.Research on High Dimensional Clustering Algorithm Based on Similarity Measurement[J].,2013,(05):30-33.
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基于相似性度量的高维数据聚类算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年05期
页码:
30-33
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Research on High Dimensional Clustering Algorithm Based on Similarity Measurement
文章编号:
1673-629X（2013）05-0030-04
作者:
王晓阳1; 张洪渊2; 沈良忠2; 池万乐2
[1]温州大学 物理与电子信息工程学院;[2]温州大学城市学院
Author(s):
WANG Xiao-yang; ZHANG Hong-yuan; SHEN Liang-zhong; CHI Wan-le
关键词:
高维数据; 相似性度量; 数据聚类
Keywords:
high dimensional data; similarity measurement; data clustering
文献标志码:
A
摘要:
高维数据空间中的高维数据相似性度量问题是一个具有挑战性的课题.针对传统数据相似性度量算法在高维数据空间的不适应性,通过对传统的距离度量方法进行分析,结合高维数据特性,提出了高维数据相似性度量函数Esim( X, Y ).将其与已有的相似性度量函数Hsim( X,Y )进行比较,得出改进的算法在高维相似性度量方面的优越性,特别是在高值数据之间与低值数据之间的相对差异方面更具优势.利用数值型数据集进行实验分析,验证了该函数在高维数据空间聚类的有效性和合理性
Abstract:
The problem of similarity measurement for high dimensional data between high dimensional spaces is a challenging issue. Ai-ming at the problems of the inapplicability of the traditional measurement in high dimensional space,the improved function Esim( X,Y ) is proposed to measure the similarity between the data in high dimensional space through analyzing and summarizing the traditional meas-urement with the properties of high dimensional data. Advantages of the improved function are obvious between high dimensional space similarity measurement comparing with Hsim( X,Y ),especially in high values and low values. The experiments by numerical dataset demonstrate that the function Esim( X,Y ) is effective and reasonable in high dimensional data clustering

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更新日期/Last Update: 1900-01-01