Research Based on Euclid Distance with Weights of K——means Algorithm
[1]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):89-92.
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
31
Number of periods:
2010 01
Page number:
89-92
Column:
Public date:
2010-01-30
- Title:
- Research Based on Euclid Distance with Weights of K——means Algorithm
- Keywords:
- k_means algorithm; clustering; weight; coefficient of variation
- CLC:
- TP391
- DOI:
- -
- 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．
- References:
-
【1]PANG N T，MICHAEL S，VIP]N K．数据挖掘导论(英文版)[M]．北京：人民邮电出版社，2006．[2]HANJ W，MICHELINE K．数据挖掘概念与技术[M]．北京：机械工业出版社，2005．[3] 沈洁，赵雷。杨季文，等．一种基于划分的层次的聚类算法[J】．计算机工程与应用，2007，43(3I)：175一177．[4]孙吉贵，刘杰，赵连字．聚类算法研究[J】．软件学万方数据92郑州大学学报(工学版)2010在报，2008．19(01)：48—61．[5] 马卫武，李念平，杨志昂．室内空气品质综合评价权重系数的确定与分析[J]．通风除尘。2004。(1 1)：9—1I．
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Last Update:
2023-03-06