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

  • Jong-Sub Lee
  • Maing-Kyu Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)

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.

Keywords

Cluster Algorithm Output Node Link Weight Learn Vector Quantization Suggested Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jong-Sub Lee
    • 1
  • Maing-Kyu Kang
    • 2
  1. 1.Department of Technical Management Information SystemsUniversity of WoosongDaejeonSouth Korea
  2. 2.Department of Information & Industrial EngineeringUniversity of HanyangAnsanSouth Korea

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