An Investigation into Fuzzy Clustering and Classification.

Abstract

Pattern recognition algorithms based on fuzzy set theory were investigated and compared to their analogs which use traditional, or crisp set theory. The fuzzy K-means clustering algorithm was investigated and the fuzzy K-nearest neighbor and fuzzy 1-nearest prototype classifier algorithms were developed. These pattern recognition algorithms produce membership assignments (values from zero to one) for the samples considered. Thus, a sample's degree of belonging in a class can be assessed via these membership assignments.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1984
Accession Number
ADA145571

Entities

People

  • M. R. Gray

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Character Recognition
  • Classification
  • Computer Programs
  • Computers
  • Data Sets
  • Electrical Engineering
  • Feature Extraction
  • Formal Languages
  • Fuzzy Sets
  • New York
  • Pattern Recognition
  • Probability
  • Set Theory
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms