A modified SOM learning algorithm for mixed types of symbolic and fuzzy data

Authors:
De-Hua Chen;Miin-Shen Yang;Wen-Liang Hung
Affiliations:
Department of Applied Mathematics, Chung Yuan Christian University, Chungli, Taiwan;Department of Applied Mathematics, Chung Yuan Christian University, Chungli, Taiwan;Graduate Institute of Computer Science, National Hsinchu University of Education, Hsin-Chu, Taiwan
Venue:
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Year:
2009

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Abstract

The Kohonen's self-organizing map (SOM) is a competitive learning neural network that uses a neighborhood lateral interaction function to discover the topological structure hidden in the data set. It is an unsupervised approach. In general, this SOM neural network is constructed as a learning algorithm for numeric (vector) data. However, except the numeric data, there are many other types of data such as symbolic and fuzzy, etc. In this paper, we first consider these feature vectors including numeric, symbolic and fuzzy data. We then create a modified SOM learning algorithm for treating these mixed types of data. Finally, we apply the modified SOM to a real example.