Self-organizing Map with Fuzzy Class Memberships
Abstract
Self-organizing maps (SOM) can be used as clustering algorithm to discover structure and similarity in data and to capture the descriptive aspect by repeated partitioning and evaluating. It has the ability to represent multidimensional data in topological mapping. If a class label is known, self-organizing map can be also used by a classifier. In this case, each neuron is assigned a class label based on the maximum class frequency and classified by a nearest neighbor strategy. The problem when using this strategy is that each pattern is treated by equal importance in counting class frequency regardless of its typicalness. But, with known class label we can take an advantage of this information by applying fuzzy set theory and assigning the fuzzy class membership into each neuron. In fact, the fuzzy-membership-label neuron gives us insight of the degree of class typicalness and distinguishes itself from a class cluster.
Recommended Citation
S. Sohn and C. H. Dagli, "Self-organizing Map with Fuzzy Class Memberships," Proceedings of SPIE - The International Society for Optical Engineering, vol. 4390, pp. 150 - 157, Society of Photo-optical Instrumentation Engineers, Jan 2001.
The definitive version is available at https://doi.org/10.1117/12.421165
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Fuzzy memberships; Learning vector quantization; Self-organizing map
International Standard Serial Number (ISSN)
0277-786X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.
Publication Date
01 Jan 2001
