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

This paper proposes a clustering method SOMAK, which is composed by Self-Organizing Maps (SOM) followed by the Ant K-means (AK) algorithm. The aim of this method is not to find an optimal clustering for the data, but to obtain a view about the structure of data clusters. SOM is an Artificial Neural Network, which has one of its characteristics the nonlinear projection. AK is a meta-heuristic approach for solving hard combinatorial optimization problems based on Ant Colony Optimization (ACO). The SOMAK has a good performance when compared with some clustering techniques and reduces the computational time.

• Type: Conference
• Year: 2009
• Where: ICANN
• Authors: Jefferson R. Souza, Teresa Bernarda Ludermir, Leandro M. Almeida
• Keywords: Ant Colony Optimization, Clustering Method Somak, ICANN 2009, Optimal Clustering