Abstract
A novel clustering algorithm based upon a SOFM neural network family is proposed in this paper. The algorithm takes full advantage of the characteristics of SOFM Neural Network family and defines a novel similarity measure, topological similarity, which help the clustering algorithm to handle the clusters with arbitrary shapes and avoid suffering from the limitations of the conventional clustering algorithms. The paper suggests another novel thought to tackle the clustering problem.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wen, J., Meng, K., Wu, H., Wu, Z. (2005). A Novel Clustering Algorithm Based upon a SOFM Neural Network Family. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_12
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DOI: https://doi.org/10.1007/11427445_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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