An improved Kohonen self-organizing map (I-KSOM) clustering algorithm for high-dimensional data sets

Momotaz Begum, Bimal Chandra Das, Md. Zakir Hossain, Antu Saha, Khaleda Akther Papry


Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen Self-Organizing Map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability prob-lems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation for-mula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.


Clustering; EISEN Cosine correlation; High-dimensional data sets; Kohonen self-organizing map; Overlapping problem;



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