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Some measures to impact on the performance of Kohonen self-organizing map

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Abstract

In the field of unsupervised learning, Self-Organizing Map (SOM) has attracted the attention of many researchers. SOM is a popular algorithm in the area of data clustering; in this paper, new algorithms are developed to find the initial weights, to assign those initial weights to the SOM grid and a new way to determine the number of clusters in the SOM algorithm. Also, a new performance measure MSRI (Modified Semantic Relevant Index) has been introduced for the SOM algorithm. For the class label datasets, the performance criteria like Classification Accuracy (CA), Quantization error (QE) and Convergence time (CT) are used to compare the proposed SOM algorithm with existing SOM algorithms. Here the existing SOM algorithms like Enhanced SOM (ESOM), SOM Particle Swarm Optimization (SOMPSO), ESOMPSO and conventional SOM are used. In addition, MSRI is used to compare the proposed SOM with the existing SOM algorithms. We have also used different image classification datasets to compare our proposed SOM and existing SOM algorithm with CA, QE, CT, and MSRI. For the non-class label dataset, the criteria like QE, CT and MSRI are employed to analyze the performance of proposed SOM with the conventional SOM algorithm. The gene index is also used to validate the number of clusters obtained by the proposed SOM algorithm. It is found that our proposed SOM algorithm has shown better performance in all cases.

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Authors and Affiliations

  1. National Institute of Technology, Tiruchirappalli, Tamilnadu, 620015, India

    Vijaya Prabhagar Murugesan

  2. Department of Management Studies, National Institute of Technology, Tiruchirappalli, Tamilnadu, 620015, India

    Punniyamoorthy Murugesan

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  1. Vijaya Prabhagar Murugesan
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Murugesan, V.P., Murugesan, P. Some measures to impact on the performance of Kohonen self-organizing map. Multimed Tools Appl 80, 26381–26409 (2021). https://doi.org/10.1007/s11042-021-10912-1

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