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17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)
Hong Kong, China
November 14-November 16
ISBN: 0-7695-2488-5
ASCII Text
x 
 
Wei Lu, Issa Traore, "Determining the Optimal Number of Clusters Using a New Evolutionary Algorithm," Tools with Artificial Intelligence, IEEE International Conference on, pp. 712-713, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05), 2005.
 
 
BibTex
x
 
@article{ 10.1109/ICTAI.2005.57,
author = {Wei Lu and Issa Traore},
title = {Determining the Optimal Number of Clusters Using a New Evolutionary Algorithm},
journal ={Tools with Artificial Intelligence, IEEE International Conference on},
volume = {0},
year = {2005},
issn = {1082-3409},
pages = {712-713},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2005.57},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Tools with Artificial Intelligence, IEEE International Conference on
TI - Determining the Optimal Number of Clusters Using a New Evolutionary Algorithm
SN - 1082-3409
SP712
EP713
A1 - Wei Lu,
A1 - Issa Traore,
PY - 2005
KW - null
VL - 0
JA - Tools with Artificial Intelligence, IEEE International Conference on
ER -
 
Wei Lu, University of Victoria
Issa Traore, University of Victoria
Estimating the optimal number of clusters for a dataset is one of the most essential issues in cluster analysis. An improper pre-selection for the number of clusters might easily lead to bad clustering outcome. In this paper, we propose a new evolutionary algorithm to address this issue. Specifically, the proposed evolutionary algorithm defines a new entropy-based fitness function, and three new genetic operators for splitting, merging, and removing clusters. Empirical evaluations using the synthetic dataset and an existing benchmark show that the proposed evolutionary algorithm can exactly estimate the optimal number of clusters for a set of data.
Citation:
Wei Lu, Issa Traore, "Determining the Optimal Number of Clusters Using a New Evolutionary Algorithm," ictai, pp.712-713, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05), 2005
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