A Fuzzy c-means Algorithm Based on an Adaptive L2 Minkowsky Distance

Rio de Janeiro, Brazil December 06-December 09

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http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.5

Fifth International Conference on Hyb ...

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	Nicomedes L. Cavalcanti Junior, "A Fuzzy c-means Algorithm Based on an Adaptive L2 Minkowsky Distance," Hybrid Intelligent Systems, International Conference on, pp. 104-109, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005.

	BibTex	x
	@article{ 10.1109/ICHIS.2005.5, author = {Nicomedes L. Cavalcanti Junior}, title = {A Fuzzy c-means Algorithm Based on an Adaptive L2 Minkowsky Distance}, journal ={Hybrid Intelligent Systems, International Conference on}, volume = {0}, year = {2005}, isbn = {0-7695-2457-5}, pages = {104-109}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.5}, 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 Fuzzy c-means Algorithm Based on an Adaptive L2 Minkowsky Distance SN - 0-7695-2457-5 SP104 EP109 A1 - Nicomedes L. Cavalcanti Junior, PY - 2005 KW - null VL - 0 JA - Hybrid Intelligent Systems, International Conference on ER -

Nicomedes L. Cavalcanti Junior, Centro de Informatica - CIn / UFPE, Brazil

An extension of the fuzzy c-means clustering algorithm based on an adaptive distance is presented. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive L2 Minkowsky distance that changes at each algorithm?s iteration. Experiments with real and synthetic data sets show the usefulness of this method.

Citation:

Nicomedes L. Cavalcanti Junior, "A Fuzzy c-means Algorithm Based on an Adaptive L2 Minkowsky Distance," his, pp.104-109, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005

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