科研汇编
上饶师范学院2010年度科研成果汇编—附录： SCI、EI、ISTP收录情况(4)
发布日期：2011-09-03 00:00:00 来源:：本站原创
发布日期：2011-09-03 00:00:00 来源:：本站原创
第4页 共11页2011-04-18 9:21
5.（陈新泉）
Accession number: 20104313325387
Title: A semi-supervised weighted clustering framework facing to hybrid attributes data
streams
Authors: Chen, Xinquan1
Author affiliation: 1 College of Mathematics and Computer Science, Shangrao Normal University, Shangrao,
Jiangxi, China
Corresponding author: Chen, X. (chenxqscut@126.com)
Source title: Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Abbreviated source title: Proc. World Congr. Intelligent Control Autom. WCICA
Monograph title: 2010 8th World Congress on Intelligent Control and Automation, WCICA 2010
Issue date: 2010
Publication year: 2010
Pages: 5988-5993
Article number: 5554528
Language: Chinese
ISBN-13: 9781424467129
Document type: Conference article (CA)
Conference name: 2010 8th World Congress on Intelligent Control and Automation, WCICA 2010
Conference date: July 7, 2010 - July 9, 2010
Conference location: Jinan, China
Conference code: 81810
Publisher: Institute of Electrical and Electronics Engineers Inc., 445 Hoes Lane / P.O. Box 1331,
Piscataway, NJ 08855-1331, United States
Abstract: In order to solve weighted clustering analysis problem of infinite hybrid attributes data streams
in finite space, it presents a weighted clustering and evolution analysis framework. This
framework is based on decision-tree classify of little sample using a semi-supervised strategy.
In order to record some necessary information of cluster group, it gives a definition of cluster
feature vector group of hybrid attributes data streams. In order to update the feature weight
group, it presents an adaptive optimization method of configuring feature weight group. It gives
some necessary discuss about weighted clustering and evolution analysis framework, which is
important to implement this framework. This framework can get better results sometimes. .
2010 IEEE.
Number of references: 11
Main heading: Cluster analysis
Controlled terms: Biology - Data communication systems - Intelligent control
