A Contribution to the Study of Ensemble of Self-Organizing Maps

Abstract

This study presents a factorial experiment to investigate the ensemble of Kohonen Self-Organizing Maps. Clusters Validity Indexes and the Mean Square Quantization Error were used as a criterion for fusing Kohonen Maps, through three different equations and four approaches. Computational simulations were performed with traditional dataset, including those with high dimensionality, not linearly separable classes, Gaussian mixtures, almost touching clusters, and unbalanced classes, from the UCI Machine Learning Repository and from Fundamental Clustering Problems Suite, with variations in map size, number of ensemble components, and the percentage of dataset bagging. The proposed method achieves a better classification than a single Kohonen Map and we applied the Wilcoxon Signed Rank Test to evidence its effectiveness.