Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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A Clustering Algorithm using Self-Organizing Feature Maps

자기 조직화 신경망을 이용한 클러스터링 알고리듬

  • Lee, Jong-Sub (Department of Technical Management Information Systems, Woosong University) ;
  • Kang, Maing-Kyu (Department of Information & Industrial Engineering, Hanyang University)
  • 이종섭 (우송대학교 IT(경영정보)학과) ;
  • 강맹규 (한양대학교 정보경영공학과)
  • Published : 2005.09.30
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Abstract

This paper suggests a heuristic algorithm for the clustering problem. Clustering involves grouping similar objects into a cluster. Clustering is used in a wide variety of fields including data mining, marketing, and biology. 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 k output-layer nodes, if they want to make k 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 can find input data in SOFMs output node and classify input data in output nodes using Euclidean distance. We use the well known IRIS data as an experimental data. Unsupervised clustering of IRIS data typically results in 15 - 17 clustering error. However, the proposed algorithm has only six clustering errors.

Keywords

References

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