|Table of Contents|

Iterative Clustering with Local and Global Regularization(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2014年03期
Page:
21-
Research Field:
计算机科学
Publishing date:

Info

Title:
Iterative Clustering with Local and Global Regularization
Author(s):
Xu Xiaolong; Wang Shitong
School of Digital Media,Jiangnan University,Wuxi 214122,China
Keywords:
convex; spectral clustering; local regularization; global regularization; iterative
PACS:
TP181
DOI:
-
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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Last Update: 2014-09-30