[1]陶莹,杨锋,刘洋,等.K 均值聚类算法的研究与优化[J].计算机技术与发展,2018,28(06):90-92.[doi:10.3969/ j. issn.1673-629X.2018.06.020]
　TAO Ying,YANG Feng,LIU Yang,et al.Research and Optimization of K-means Clustering Algorithm[J].,2018,28(06):90-92.[doi:10.3969/ j. issn.1673-629X.2018.06.020]
点击复制

K 均值聚类算法的研究与优化()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年06期
页码:
90-92
栏目:
智能、算法、系统工程
出版日期:
2018-06-10

文章信息/Info

Title:
Research and Optimization of K-means Clustering Algorithm
文章编号:
1673-629X(2018)06-0090-03
作者:
陶莹; 杨锋; 刘洋; 戴兵
广西大学 计算机与电子信息学院,广西 南宁 530004
Author(s):
TAO Ying; YANG Feng; LIU Yang; DAI Bing
School of Computer and Electronic Information,Guangxi University,Nanning 530004,China
关键词:
数据挖掘; K 均值聚类; 中心点; 误差平方和
Keywords:
data mining; K-means; center point; error sum of squares
分类号:
TP393
DOI:
10.3969/ j. issn.1673-629X.2018.06.020
文献标志码:
A
摘要:
聚类分析是数据挖掘的重要组成部分,K 均值聚类算法是聚类分析方法中一种基本的划分式方法,也是无监督的机器学习方法。 其具有效率高、容易理解和实现等优点,同时,可以对多种数据类型进行聚类,广泛应用于诸多领域。 但是,K 均值聚类算法也有一些局限性。 算法中合理的 k 值难以确定,而且算法选择初始聚类中心的随机性会导致聚类结果不稳定,同时,算法对噪声和离群点数据也有很强的敏感性。 为了解决初始聚类中心随机性的问题,通过全局化思想对 K均值聚类算法进行了改进,改进的聚类效果评价使用常用的误差平方和准则。 实验结果表明,相较于一般的 K 均值聚类算法,全局 K 均值聚类算法得到了更好的聚类效果,同时提升了算法的稳定性。
Abstract:
Clustering analysis is an important part of data mining. The K-means clustering algorithm is a basic partition method of clustering analysis,and it is also an unsupervised machine learning method with the advantages of high efficiency,easy understanding and implementing. At the same time,the clustering data type can be various,so it is widely used in many fields. However,the K-means clustering algorithm exists some limitations. For example,the reasonable value of k is difficult to determine,and choosing the initial clustering center is random,which can lead to the result unstable,also with strong sensitivity to noise and outliers. In order to solve the problem of the randomness for initial clustering center,we improve the K-means clustering algorithm through the idea of global change. The evaluation criterion of the clustering effect is the error sum of squares. Experiment shows that compared with normal K-means clustering algorithm,the global K-means clustering algorithm can get better clustering effect,while increasing its stability.

相似文献/References:

[1]项响琴 汪彩梅.基于聚类高维空间算法的离群数据挖掘技术研究[J].计算机技术与发展,2010,(01):120.
　XIANG Xiang-qin,WANG Cai-mei.Study of Outlier Data Mining Based on CLIQUE Algorithm[J].,2010,(06):120.
[2]李雷 丁亚丽 罗红旗.基于规则约束制导的入侵检测研究[J].计算机技术与发展,2010,(03):143.
　LI Lei,DING Ya-li,LUO Hong-qi.Intrusion Detection Technology Research Based on Homing - Constraint Rule[J].,2010,(06):143.
[3]吉同路 柏永飞 王立松.住宅与房地产电子政务中数据挖掘的应用研究[J].计算机技术与发展,2010,(01):235.
　JI Tong-lu,BAI Yong-fei,WANG Li-song.Study and Application of Data Mining in E-government of House and Real Estate Industry[J].,2010,(06):235.
[4]杨静 张楠男 李建 刘延明 梁美红.决策树算法的研究与应用[J].计算机技术与发展,2010,(02):114.
　YANG Jing,ZHANG Nan-nan,LI Jian,et al.Research and Application of Decision Tree Algorithm[J].,2010,(06):114.
[5]赵裕啸 倪志伟 王园园 伍章俊.SQL Server 2005数据挖掘技术在证券客户忠诚度的应用[J].计算机技术与发展,2010,(02):229.
　ZHAO Yu-xiao,NI Zhi-wei,WANG Yuan-yuan,et al.Application of Data Mining Technology of SQL Server 2005 in Customer Loyalty Model in Securities Industry[J].,2010,(06):229.
[6]张笑达 徐立臻.一种改进的基于矩阵的频繁项集挖掘算法[J].计算机技术与发展,2010,(04):93.
　ZHANG Xiao-da,XU Li-zhen.An Advanced Frequent Itemsets Mining Algorithm Based on Matrix[J].,2010,(06):93.
[7]王爱平 王占凤 陶嗣干 燕飞飞.数据挖掘中常用关联规则挖掘算法[J].计算机技术与发展,2010,(04):105.
　WANG Ai-ping,WANG Zhan-feng,TAO Si-gan,et al.Common Algorithms of Association Rules Mining in Data Mining[J].,2010,(06):105.
[8]张广路 雷景生 吴兴惠.一种改进的Apriori关联规则挖掘算法（英文）[J].计算机技术与发展,2010,(06):84.
　ZHANG Guang-lu,LEI Jing-sheng,WU Xing-hui.An Improved Apriori Algorithm for Mining Association Rules[J].,2010,(06):84.
[9]吴楠 胡学钢.基于聚类分区的序列模式挖掘算法研究[J].计算机技术与发展,2010,(06):109.
　WU Nan,HU Xue-gang.Research on Clustering Partition-Based Approach of Sequential Pattern Mining[J].,2010,(06):109.
[10]吴青 傅秀芬.水平分布数据库的正负关联规则挖掘[J].计算机技术与发展,2010,(06):113.
　WU Qing,FU Xiu-fen.Positive and Negative Association Rules Mining on Horizontally Partitioned Database[J].,2010,(06):113.

更新日期/Last Update: 2018-08-16