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A Clustering Algorithm Using the Ordered Weight of Self-Organizing Feature Maps  

Lee Jong-Sup (울산광역시중소기업종합지원센터 S/W지원팀)
Kang Maing-Kyu (한양대학교 정보경영공학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.31, no.3, 2006 , pp. 41-51 More about this Journal
Abstract
Clustering is to group similar objects into clusters. Until now there are a lot of approaches using Self-Organizing feature Maps (SOFMS) But they have problems with a small output-layer nodes and initial weight. For example, one of them is a one-dimension map of c output-layer nodes, if they want to make c clusters. This approach has problems to classify elaboratively. 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 un find input data in SOFMs output node and classify input data in output nodes using Euclidean distance. The proposed algorithm was tested on well-known IRIS data and TSPLIB. The results of this computational study demonstrate the superiority of the proposed algorithm.
Keywords
Clustering; Self-Organizing Feature Maps; Euclidean Distance; IRIS; TSPLIB;
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