REDUCTION OF CLUSTERING PROBLEM TO PATTERN RECOGNITION,

Abstract

The computing time required by most of the clustering programs becomes prohibitively long as the number of objects to be classified increases. It is shown to be effective in overcoming this difficulty to select a small number of 'representative' objects first and to apply the clustering program on them. The non-representative objects are thereafter placed in the generated classes by the pattern recognition technique, where the role of paradigms (class-samples) is played by the representative objects. The representative objects are those which have large components in the feature-subspace in the sense of self-featuring information compression. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jan 29, 1968
Accession Number
AD0684625

Entities

People

  • Satosi Watanabe
  • Tadao Takekawa
  • Tsuguchika Kaminuma

Organizations

  • University of HawaiĘ»i System

Tags

DTIC Thesaurus Topics

  • Clustering
  • Pattern Recognition
  • Recognition

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Instructional Design and Training Evaluation.
  • Systems Analysis and Design

Technology Areas

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