Journal of the Korean Operations Research and Management Science Society (한국경영과학회지)

The Korean Operations Research and Management Science Society (한국경영과학회)
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A Clustering Algorithm Using the Ordered Weight of Self-Organizing Feature Maps

자기조직화 신경망의 정렬된 연결강도를 이용한 클러스터링 알고리즘

  • 이종섭 (울산광역시중소기업종합지원센터 S/W지원팀) ;
  • 강맹규 (한양대학교 정보경영공학과)
  • Published : 2006.09.01
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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 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

References

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