[1]许小龙,王士同.基于局部和全局信息的正则化迭代聚类[J].南京师大学报(自然科学版),2014,37(03):21.
　Xu Xiaolong,Wang Shitong.Iterative Clustering with Local and Global Regularization[J].Journal of Nanjing Normal University(Natural Science Edition),2014,37(03):21.
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基于局部和全局信息的正则化迭代聚类()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第37卷
期数:
2014年03期
页码:
21
栏目:
计算机科学
出版日期:
2014-09-30

文章信息/Info

Title:
Iterative Clustering with Local and Global Regularization
作者:
许小龙; 王士同
江南大学数字媒体学院,江苏 无锡 214122
Author(s):
Xu Xiaolong; Wang Shitong
School of Digital Media,Jiangnan University,Wuxi 214122,China
关键词:
凸形; 谱聚类; 局部正则化; 全局正则化; 迭代
Keywords:
convex; spectral clustering; local regularization; global regularization; iterative
分类号:
TP181
文献标志码:
A
摘要:
聚类是一种高效的数据分析方法,经典的K-means算法只适用于类簇为凸形的数据集,谱聚类算法虽然避免了K-means的一些缺点,但相似度中的参数设置问题以及较高的计算、存储复杂度对聚类有所限制.基于局部和全局信息的正则化迭代聚类,先取部分数据作为一个整体聚类,然后逐渐加入少量数据进行迭代求解.该方法继承传统谱聚类的优点,充分利用局部正则化和全局正则化信息,通过迭代方式求解使较大规模数据聚类成为可能.通过实验对比结果显示,该算法有良好的聚类效果.
Abstract:
Clustering is an efficient method of data analysis,K-means method is one of the most popular algorithms.The algorithm only works when the cluster of data is convex.Spectral clustering avoids the problems of K-means method,however,parameters settings in similarity calculation,complex calculation and storage complexity all constraint the effectiveness of spectral clustering.In this paper,Iterative clustering with local and global regularization is proposed.In this method,we conduct a cluster with a part of data,and then we add a small amount of remaining data gradually to the iterative calculation.The proposed method has the advantages of traditional spectral clustering,exploring both the local and global regularization,and achieve the effective clustering for Large-scale data by an iteration method.Experimental results on several data sets show the greater performance on the method.

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备注/Memo

备注/Memo:
收稿日期:2014-02-15.
基金项目:江苏省自然科学基金(BK2011417).
通讯联系人:王士同,教授,研究方向:人工智能、模式识别、生物信息.E-mail:wxwangst@aliyun.com
更新日期/Last Update: 2014-09-30