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The Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image analysis, and many others. In conventional SOM, the weights of the winner and its neighboring neurons are updated regardless of their distance from the input vector. In the proposed SOM, the farthest and nearest neurons from among the 1-neighborhood of the winner neuron, and also the winning frequency of each neuron are found out and taken into account while updating the weight. This new SOM isdoi:10.1016/j.aej.2014.09.007 fatcat:wibct2edozgapeafbjqcilqw5m