A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means

Authors:
Jefferson R. Souza;Teresa B. Ludermir;Leandro M. Almeida
Affiliations:
Center of Informatics, Federal University of Pernambuco, Recife, Brazil 50732-970;Center of Informatics, Federal University of Pernambuco, Recife, Brazil 50732-970;Center of Informatics, Federal University of Pernambuco, Recife, Brazil 50732-970
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

Citing 6
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Abstract

This paper proposes a clustering method SOMAK, which is composed by Self-Organizing Maps (SOM) followed by the Ant K-means (AK) algorithm. SOM is an Artificial Neural Network (ANN), which has one of its characteristics, the nonlinear projection from a high dimensionality of the sensorial space. AK is based in the Ant Colony Optimization (ACO), which is a recently proposed meta-heuristic approach for solving hard combinatorial optimization problems. The AK algorithm modifies the K-means on locating the objects and these are then clustered according to the probabilities which in turn are updated by the pheromone. The SOMAK has a good performance when compared with some clustering techniques and reduces the computational time.