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7th International Conference on Hybrid Intelligent Systems (HIS 2007)
A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances
Kaiserslautern, Germany
September 17-September 19
ISBN: 0-7695-2946-1
ASCII Text
x 
 
Renata M. C. R. de Souza, Francisco A. T. de Carvalho, "A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances," Hybrid Intelligent Systems, International Conference on, pp. 168-173, 7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007.
 
 
BibTex
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@article{ 10.1109/HIS.2007.13,
author = {Renata M. C. R. de Souza and Francisco A. T. de Carvalho},
title = {A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances},
journal ={Hybrid Intelligent Systems, International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2946-1},
pages = {168-173},
doi = {http://doi.ieeecomputersociety.org/10.1109/HIS.2007.13},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Hybrid Intelligent Systems, International Conference on
TI - A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances
SN - 0-7695-2946-1
SP168
EP173
A1 - Renata M. C. R. de Souza,
A1 - Francisco A. T. de Carvalho,
PY - 2007
KW - null
VL - 0
JA - Hybrid Intelligent Systems, International Conference on
ER -
 
Renata M. C. R. de Souza, Cidade Universitaria, Brazil
Francisco A. T. de Carvalho, Cidade Universitaria, Brazil
This work presents a clustering method for mixed feature-type symbolic data. The presented method needs a previous pre-processing step to transform mixed symbolic data into modal symbolic data. The dynamic clustering algorithm with adaptive distances has then as input a set of vectors of modal symbolic data (weight distributions) and furnishes a partition and a prototype to each class by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives based on adaptive squared Euclidean distances. Examples with synthetic symbolic data sets and an application with a real symbolic data sets show the usefulness of this method.
Citation:
Renata M. C. R. de Souza, Francisco A. T. de Carvalho, "A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances," his, pp.168-173, 7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007
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