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18th International Conference on Pattern Recognition (ICPR'06) Volume 1
A K-means-based Algorithm for Projective Clustering
Hong Kong
August 20-August 24
ISBN: 0-7695-2521-0
ASCII Text
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Mohamed Bouguessa, Shengrui Wang, Qingshan Jiang, "A K-means-based Algorithm for Projective Clustering," Pattern Recognition, International Conference on, vol. 1, pp. 888-891, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006.
 
 
BibTex
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@article{ 10.1109/ICPR.2006.88,
author = {Mohamed Bouguessa and Shengrui Wang and Qingshan Jiang},
title = {A K-means-based Algorithm for Projective Clustering},
journal ={Pattern Recognition, International Conference on},
volume = {1},
year = {2006},
issn = {1051-4651},
pages = {888-891},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.88},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - A K-means-based Algorithm for Projective Clustering
SN - 1051-4651
SP888
EP891
A1 - Mohamed Bouguessa,
A1 - Shengrui Wang,
A1 - Qingshan Jiang,
PY - 2006
KW - null
VL - 1
JA - Pattern Recognition, International Conference on
ER -
 
Mohamed Bouguessa, University of Sherbrooke, Canada
Shengrui Wang, Xiamen University, Fujian 361005, China
Qingshan Jiang, Xiamen University, Fujian 361005, China
In this paper, a new algorithm for projective clustering is proposed. The algorithm consists of two phases. The first phase performs attribute relevance analysis by detecting dense regions in each attribute, thereby allowing irrelevant attributes and outliers to be captured and eliminated. Starting from the results of the first phase, the second phase aims to uncover clusters in different subspaces. The clustering process is based on the k-means algorithm, with the computation of distance restricted to subsets of attributes where object values are dense.
Citation:
Mohamed Bouguessa, Shengrui Wang, Qingshan Jiang, "A K-means-based Algorithm for Projective Clustering," icpr, vol. 1, pp.888-891, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006
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