Open AccessAn improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets
Author(s) -
Momotaz Begum,
Bimal Chandra Das,
Md. Zakir Hossain,
Antu Saha,
Khaleda Akther Papry
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i1.pp600-610
Subject(s) - self organizing map , cluster analysis , computer science , pattern recognition (psychology) , data mining , artificial intelligence , robustness (evolution) , algorithm , biology , biochemistry , gene
Manipulating high-dimensional data is a major research challenge in the eld 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 problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. 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 modied KSOM in terms of predictive performance with topographic and quantization error.