A New Competitive Strategy for Self Organizing Map Learning

This paper presents a new learning strategy for the clustering algorithms based on Self-Organizing Map. Our contribution relies on the competitive phase of this unsupervised learning algorithm and proposes a new strategy for choosing the most active cell/neuron. This new strategy is to choose the most active neuron taking into account its historical activations, learned in a voting matrix from the dataset. Indeed, the use of this historic neighbourhood, allows introducing of topological constraints in the final geometry of the map. This new unsupervised learning approach allows discovering of data structure with a better quality (lower topographic error and better clustering purity). The proposed approach was validated on multiple datasets of different sizes and complexities, and the experimental validation shows promising results.