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International Conference on Computing: Theory and Applications (ICCTA'07)
A Fuzzy Genetic Clustering Technique Using a New Symmetry Based Distance for Automatic Evolution of Clusters
Kolkata, India
March 05-March 07
ISBN: 0-7695-2770-1
ASCII Text
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Sriparna Saha, Sanghamitra Bandyopadhyay, "A Fuzzy Genetic Clustering Technique Using a New Symmetry Based Distance for Automatic Evolution of Clusters," International Conference on Computing: Theory and Applications, pp. 309-314, International Conference on Computing: Theory and Applications (ICCTA'07), 2007.
 
 
BibTex
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@article{ 10.1109/ICCTA.2007.5,
author = {Sriparna Saha and Sanghamitra Bandyopadhyay},
title = {A Fuzzy Genetic Clustering Technique Using a New Symmetry Based Distance for Automatic Evolution of Clusters},
journal ={International Conference on Computing: Theory and Applications},
volume = {0},
year = {2007},
isbn = {0-7695-2770-1},
pages = {309-314},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICCTA.2007.5},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - International Conference on Computing: Theory and Applications
TI - A Fuzzy Genetic Clustering Technique Using a New Symmetry Based Distance for Automatic Evolution of Clusters
SN - 0-7695-2770-1
SP309
EP314
A1 - Sriparna Saha,
A1 - Sanghamitra Bandyopadhyay,
PY - 2007
KW - Clustering
KW - Cluster Validity Index
KW - Point Symmetry
KW - Kd-tree
KW - Genetic Algorithm
KW - Variable String Length
VL - 0
JA - International Conference on Computing: Theory and Applications
ER -
 
Sriparna Saha, Indian Statistical Institute, India
Sanghamitra Bandyopadhyay, Indian Statistical Institute, India
In this paper a fuzzy point symmetry based genetic clustering technique (Fuzzy-VGAPS) is proposed which can determine the number of clusters present in a data set as well as a good fuzzy partitioning of the data. A new fuzzy cluster validity index, FSym-index, which is based on the newly developed point symmetry based distance is also proposed here. FSym-index provides a measure of goodness of clustering on different fuzzy partitions of a data set. Maximum value of FSym-index corresponds to the proper clustering present in a data set. The flexibility of Fuzzy-VGAPS is utilized in conjunction with the fuzzy FSym-index to determine the number of clusters present in a data set as well as a good fuzzy partition of the data. The results of the fuzzy VGAPS are compared with those obtained by fuzzy version of variable string length genetic clustering technique (Fuzzy-VGA) optimizing XB-index. The effectiveness of the Fuzzy-VGAPS is demonstrated on four artificial data sets and two real-life data sets.
Index Terms:
Clustering, Cluster Validity Index, Point Symmetry, Kd-tree, Genetic Algorithm, Variable String Length
Citation:
Sriparna Saha, Sanghamitra Bandyopadhyay, "A Fuzzy Genetic Clustering Technique Using a New Symmetry Based Distance for Automatic Evolution of Clusters," iccta, pp.309-314, International Conference on Computing: Theory and Applications (ICCTA'07), 2007
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