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An Improved Cluster Labeling Method for Support Vector Clustering
March 2005 (vol. 27 no. 3)
pp. 461-464
ASCII Text
x 
 
Jaewook Lee, Daewon Lee, "An Improved Cluster Labeling Method for Support Vector Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 461-464, March, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.47,
author = {Jaewook Lee and Daewon Lee},
title = {An Improved Cluster Labeling Method for Support Vector Clustering},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {3},
issn = {0162-8828},
year = {2005},
pages = {461-464},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.47},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - An Improved Cluster Labeling Method for Support Vector Clustering
IS - 3
SN - 0162-8828
SP461
EP464
EPD - 461-464
A1 - Jaewook Lee,
A1 - Daewon Lee,
PY - 2005
KW - Clustering
KW - unsupervised learning method
KW - support vector machines.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
The support vector clustering (SVC) algorithm is a recently emerged unsupervised learning method inspired by support vector machines. One key step involved in the SVC algorithm is the cluster assignment of each data point. A new cluster labeling method for SVC is developed based on some invariant topological properties of a trained kernel radius function. Benchmark results show that the proposed method outperforms previously reported labeling techniques.

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Index Terms:
Clustering, unsupervised learning method, support vector machines.
Citation:
Jaewook Lee, Daewon Lee, "An Improved Cluster Labeling Method for Support Vector Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 461-464, March 2005, doi:10.1109/TPAMI.2005.47
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