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 language | English |
|---|---|
| Pages (from-to) | 447-451 |
| Number of pages | 5 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 1 |
| Publication status | Published - 1995 Dec 1 |
| Event | Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Part 2 (of 5) - Vancouver, BC, Can Duration: 1995 Oct 22 → 1995 Oct 25 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Hardware and Architecture