Abstract
Clustering is to group similar objects into clusters. Until now there are a lot of approaches using Self-Organizing Feature Maps(SOFMs). But theyhave problems with a small output-layer nodes and initial weight. This paper suggests one-dimensional output-layer nodes in SOFMs. The number of output-layer nodes is more than those of clusters intended to find and the order of output-layer nodes is ascending in the sum of the output-layer node’s weight. We can find input data in SOFMs output node and classify input data in output nodes using the Euclidean Distance. The suggested algorithm was tested on well-known IRIS data and machine-part incidence matrix. The results of this computational study demonstrate the superiority of the suggested algorithm.
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Lee, JS., Kang, MK. (2006). A Clustering Algorithm Using the Ordered Weight Sum of Self-Organizing Feature Maps. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751595_94
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DOI: https://doi.org/10.1007/11751595_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34075-1
Online ISBN: 978-3-540-34076-8
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