计算机技术与发展2018，Vol.28Issue(6)：90-92,3.DOI:10.3969/j.issn.1673-629X.2018.06.020
K均值聚类算法的研究与优化
Research and Optimization of K-means Clustering Algorithm
摘要
Abstract
Clustering analysis is an important part of data mining. The K-means clustering algorithm is a basic partition method of cluste-ring analysis,and it is also an unsupervised machine learning method with the advantages of high efficiency,easy understanding and im-plementing. At the same time,the clustering data type can be various,so it is widely used in many fields. However,the K-means cluste-ring 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 evalua-tion criterion of the clustering effect is the error sum of squares. Experiment shows that compared with normal K-means clustering algo-rithm,the global K-means clustering algorithm can get better clustering effect,while increasing its stability.关键词
数据挖掘/K均值聚类/中心点/误差平方和Key words
data mining/K-means/center point/error sum of squares分类
信息技术与安全科学引用本文复制引用
陶莹,杨锋,刘洋,戴兵..K均值聚类算法的研究与优化[J].计算机技术与发展,2018,28(6):90-92,3.基金项目
广西壮族自治区中青年教师基础能力提升项目(KY2016YB026) （KY2016YB026）
广西自然科学基金(2014GXNSFBA118274) （2014GXNSFBA118274）
