The Performance of an Autonomous Clustering Technique

  • Yoshiharu Sato
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Recently, a multi-agents system has been discussed in the field of adaptive control. An autonomous clustering is considered to be a multiple agents system which constructs clusters by moving each pair of objects closer or farther according to their relative similarity to all of the objects.

In this system, the objects correspond to autonomous agents, and the similarity relation is regarded as the environment. Defining a suitable action rule for the multi-agents, the clusters are constructed automatically.

In this paper, we discuss the ability of the detection of clusters by the autonomous clustering technique through the concrete examples.


Covariance Pier 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. ARBIB, M.A. (ed.)(1995). The Handbook of Brain Theory and Neural Networks. The MIT Press.Google Scholar
  2. BOCK, H. H. (1998). Clustering and Neural Networks. Advances in Data Science and Classification (A. Rizzi et al. ed.), 265–277.Google Scholar
  3. KOHONEN, T. (1995). Self-organizing maps. Springer, New York.CrossRefGoogle Scholar
  4. ITOH, H. (1998). Berthing Control with Multi-Agent System. Jour. Soc. Naval Architechture of Japan, Vol. 184, 639–647Google Scholar
  5. SATO, Y. (2000). An Autonomous Clustering Technique. Data Analysis, Classification and Related Methods (H.A.L.Kiers et al. ed.), 23–28.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Yoshiharu Sato
    • 1
  1. 1.Division of Systems and InformationHokkaido UniversitySapporoJapan

Personalised recommendations