Self-organizing concept maps

Research output: Contribution to journalConference article

9 Citations (Scopus)

Abstract

Self-Organizing Concept Maps (SOCOMs) based on a neural network model have been proposed in this paper. They can arrange concepts or words in a map space using Kohonen's self-organizing map algorithm. One of the most important advantages of the proposed maps is that they employ the idea of k-nearest neighbor (k-NN): they do not require all of the data among concepts or words. We propose two kinds of SOCOMs: one is a metric SOCOM, another is a non-metric one. The metric SOCOM uses the information about the metric data such as similarity. The non-metric one uses the information about the rank order of similarity among items. The combination of the idea of k-NN and a non-metric SOCOM is effective to relax the severe requirements on data: it does not require all of the detailed metric information among concepts or words. Computer simulation results have shown the effectiveness of the proposed SOCOM.

Original languageEnglish
Pages (from-to)447-451
Number of pages5
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
Publication statusPublished - 1995 Dec 1
EventProceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Part 2 (of 5) - Vancouver, BC, Can
Duration: 1995 Oct 221995 Oct 25

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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