Cluster and Classify: A Conceptual Approach

  • Carlo Misiak
Conference paper
Part of the NATO ASI Series book series (volume 61)


From the platform of a data set that was gathered in a complex problem solving task, this paper presents preliminary results of the evaluation of the feasibility of establishing conceptual clustering schemes within the structure of an Object Oriented Programming System, namely KEE (Knowledge Engineering Environment). It is shown that both adequate data description and representation are crucial in order to successfully develop the method. The conclusion is that it is feasible to generate description primitives from the data. However, it is not quite clear yet how to evaluate the quality of potential solutions of an inductive process.


Conceptual Cluster Domain Constraint Inferential Knowledge Oriented Method Conceptual Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. deKLEER, J. (1986) An Assumption-Based TMS, Artificial Intelligence, 28, 1, 127–162.CrossRefGoogle Scholar
  2. FIKES, R.E., and KEHLER, T. (1985), The role of frame-based representation in reasoning, Communications of the ACM, 28, 9, 904–920.CrossRefGoogle Scholar
  3. KLUWE, R.H., MISIAK, C., and REIMANN, H. (1984), Lernvorgänge beim Umgang mit Systemen: Die Ausbildung subjektiver Ordnungsstrukturen durch Erfahrungen beim Umgang mit umfangreichen Systemen, Bericht aus dem Fachbereich Pädagogik der Hochschule der Bundeswehr, Hamburg.Google Scholar
  4. 4.
    MICHALSKI, R.S. (1980), Pattern Recognition as Rule-Guided Inductive Inference, IEEE Trans. Pattern Anal. Mach. Intell., PAMI 2, 349–361.CrossRefMATHGoogle Scholar
  5. MICHALSKI, R.S. (1983), A Theory and Methodology of Inductive Learning, in: Machine Learning, eds. R.S. Michalski, J.G. Carbonell and T.M. Mitchell, Tioga, Palo Alto, 83–134.Google Scholar
  6. MINSKY, M. (1975), A Framework for Representing Knowledge, in: The Psychology of Computer Vision, ed. P.H. Winston, McGraw-Hill, New York, 211–277.Google Scholar
  7. MISIAK, C., and KLUWE, R.H. (1986), CSC - Complex System Control, Bericht aus dem Fachbereich Pädagogik der Universität der Bundeswehr, Hamburg.Google Scholar
  8. NEWELL, A. (1973), Production Systems: Models of Control Structures, in: Visual Information Processing, ed. W.G. Chase, Academic Press, New York, 463–526.Google Scholar
  9. SHEKAR, B., NARASIMHA MURTY, M., and KRISHNA, G. (1989), Structural Aspects of Semantic-Directed Clusters, Pattern Recognition, 22, I, 65–74.CrossRefMATHMathSciNetGoogle Scholar
  10. SIMON, H.A. (1983), Why Should Machines Learn?, in: Machine Learning, eds. R.S. Michalski, J.G. Carbonell and T.M. Mitchell, Tioga, Palo Alto, 25–37.Google Scholar
  11. SINOWJEW, A., and WESSEL, H. (1975), Logische Sprachregeln, Deutscher Verlag der Wissenschaften, Berlin.MATHGoogle Scholar
  12. STEFIK, M. (1979), An examination of a frame-structured system, in: Proceedings of the 6th International Joint Conference on Artificial Intelligence (Tokyo), International Joint Conference on Artificial Intelligence, 845–852.Google Scholar
  13. STROHSCHNEIDER, S. (1986), Zur Stabilität und Validität von Handeln in komplexen Realitätsbereichen, Sprache & Kognition, 1, 42–48.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Carlo Misiak
    • 1
  1. 1.Institut für KognitionsforschungUniversität der Bundeswehr HamburgGermany

Personalised recommendations