%0 Journal Article
%T Sequential Combination Methods forData Clustering Analysis<br>Sequential Combination Methods for Data Clustering Analysis
%A Ching YSuen
%A <br>钱涛
%A Ching Y.Suen
%A 唐远炎
%J 计算机科学技术学报
%D 2002
%I 
%X This paper proposes the use of more than one clustering method to improve clustering performance. Clustering is an optimization procedure based on a specific clustering criterion. Clustering combination can be regarded as a technique that constructs and processes multiple clustering criteria. Since the global and local clustering criteria are complementary rather than competitive, combining these two types of clustering criteria may enhance the clustering performance. In our past work, a multi-objective programming based simultaneous clustering combination algorithm has been proposed, which incorporates multiple criteria into an objective function by a weighting method, and solves this problem with constrained nonlinear optimization programming. But this algorithm has high computational complexity. Here a sequential combination approach is investigated, which first uses the global criterion based clustering to produce an initial result, then uses the local criterion based information to improve the initial result with a probabilistic relaxation algorithm or linear additive model. Compared with the simultaneous combination method, sequential combination has low computational complexity. results on some simulated data and standard test data are reported. It appears that clustering performance improvement can be achieved at low cost through sequential combination. This work is supported in part by the China State Education Commission Laboratory for Image Processing and Intelligent Control under grant TKLJ9901, and Zhejiang Education Commission under grant No. 19990119. QIAN Yuntao received the B.E. and M.E. degrees in automatic control from Xi’an Jiaotong University in 1989 and 1992 respectively, and his Ph.D. degree in signal processing from Xidian University in 1996. From 1996 to 1998, he was a postdoctoral fellow in Northwestern Polytechnical University. Since 1998, he has been an associate professor in Department of Computer Science, Zhejiang University. From 1999 to 2001, he was a visiting scholar to the Centre of Pattern Recognition and Machine Intelligence, Concordia University, Canada, and also to Department of Computer Science, Hong Kong Baptist University. He has published more than 20 technical papers in academic journals and conference proceedings. His present research interests include data clustering analysis, pattern recognition, image processing, wavelet theory, and neural networks. Ching Y. Suen received his M.S. degree in engineering from the University of Hong Kong, followed by the Ph.D. degree from the University of British Columbia, Canada. In 1972, he joined the Department of Computer Science, Concordia University, Canada, and became a professor in 1979. He is the director of the Centre of Pattern Recognition and Machine Intelligence. He is the author/editor of 11 books and more than 260 papers on subjects ranging from computer vision and handwriting recognition to expert system and computational linguistics. He is the founder
%K clustering combination
%K probabilistic relaxation
%K linear additive model
%K clustering criterion<br>算法
%K 连续合并法
%K 数据聚类分析
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=6DD94C7618D02E732C0790558C427C12&yid=C3ACC247184A22C1&vid=BCA2697F357F2001&iid=0B39A22176CE99FB&sid=2B25C5E62F83A049&eid=2B25C5E62F83A049&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=0&reference_num=25