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Proceedings Paper

Clustering analysis of compact overlapped clusters using fuzzy reinforced learning vector quantization technique
Author(s): Wenhuan Xu; Nan Bai; Qiang Huang; Zhen Ji
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Paper Abstract

A fuzzy reinforced learning vector quantization (FRLVQ) algorithm has been recently developed and proved to work successfully in an image compression application. Due to the close affinity between vector quantization and clustering analysis, as both of them group an unlabelled data set into a certain number of clusters such that data within the same cluster have a high degree of similarity, FRLVQ is used as the first stage for clustering analysis in the proposed method, named XHJ-method, in this paper. The simulation results show that this new method works well for the traditional Iris data and an artificial data set based on V29 QAM. Both data sets contain un-equally sized and spaced clusters.

Paper Details

Date Published: 3 November 2005
PDF: 6 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60431Q (3 November 2005); doi: 10.1117/12.654933
Show Author Affiliations
Wenhuan Xu, Shenzhen Univ. (China)
Nan Bai, Shenzhen Univ. (China)
Qiang Huang, Shenzhen Univ. (China)
Zhen Ji, Shenzhen Univ. (China)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

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