A clustering algorithm using the ordered weight sum of self-organizing feature maps

Authors:
Jong-Sub Lee;Maing-Kyu Kang
Affiliations:
Department of Technical Management Information Systems, University of Woosong, Daejeon, South Korea;Department of Information & Industrial Engineering, University of Hanyang, Ansan, South Korea
Venue:
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Year:
2006

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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.