Self-organizing map with fuzzy class memberships

S. Sohn, C. H. Dagli

Research output: Contribution to journalConference article

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)150-157
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4390
DOIs
StatePublished - Jan 1 2001
EventApplications and Science of Computational Intelligence IV - Orlando, FL, United States
Duration: Apr 17 2001Apr 18 2001

Keywords

  • Fuzzy memberships
  • Learning vector quantization
  • Self-organizing map

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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