A two stage clustering method combining self-organizing maps and ant k-means

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. © 2009 Springer Berlin Heidelberg.