Decision Theoretic Rough Set-Based Neighborhood for Self-Organizing Map

A decision theoretic rough set-based neighborhood selection process is developed for self-organizing maps. While the neighborhood of the winner neuron is selected based on the probability of its associativity to the winner neuron, the selected neighborhood is updated using a new method which combines the probability of its associativity and the Gaussian function. This approach provides better results as compared to self-organizing map and other clustering algorithms on several real-life datasets. The results are evaluated in terms of DB index, Dunn index, quantization error, ARI, and NMI.