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On the phenomenon of flattening “flexible prediction” concept hierarchy

  • Part II Selected Contributions
  • Conference paper
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Fundamentals of Artificial Intelligence Research (FAIR 1991)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 535))

Abstract

The Incremental Concept Formation method, as described in [19] is claimed therein to be “able to formulate diagnostically useful categories even without class information”, “given real world data on heart disease”. We suggest in this paper that the method does not derive categories from the data but from primary and derived attribute selection by showing that equal treatment of all attributes leads to a flat (one level) concept hierarchy.

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References

  1. Hartigan J.A., Clustering Algorithms, John WilleySons, New York 1975.

    Google Scholar 

  2. Anderberg M.R., Cluster Analysis for Applications, Academic Press, New York, 1973

    Google Scholar 

  3. Watanabe S., Pattern Recognition, Human and Machine, (1987)

    Google Scholar 

  4. Michalski R.S., Stepp R.E., Automated construction of classification, conceptual clustering versus numerical taxonomy, IEEE Transactions on PAMI-5, 1983, pp. 396–410

    Google Scholar 

  5. Michalski R.S., Stepp R.E., III. Learning from observations: conceptual clustering, in Machine Learning: An Artificial Intelligence Approach, Michalski R.S., Carbonell J.G., Mitchell T.M. (Eds), Morgan Kaufmann Publishers Inc., Los Altos, 1983

    Google Scholar 

  6. Stepp R.E., Michalski R.S., Conceptual clustering:Conceptual cluster-ing of structured objects, Artificial Intelligence 28,1, 1986, pp. 43–70

    Google Scholar 

  7. Stepp R.E., Michalski R.S., Conceptual clustering: Inventing goal-oriented classifications of structured objects, in Machine Learning II: An Artificial Intelligence Approach, Michalski R.S., Carbonell J.G., Mitchell T.M. (Eds), Morgan Kaufmann Publ.Inc., Los Altos, 1986

    Google Scholar 

  8. Dale B., On the comparison of conceptual clusterimng and numerical taxonomy, IEEE Transactions on PAMI-7, March 1985

    Google Scholar 

  9. Gluck M., Corter J., Information, uncertainty and the utility of categories, Proc. 7 Ann. Conf. of the Cognitive Sci., Ivrine CA (1985), 283–7

    Google Scholar 

  10. Kolodner J.L., Retrieval and Organizational strategies in conceptual memory: A computer model, Lawrence Erlbaum Associates Publ, London 1984

    Google Scholar 

  11. Fisher D., Langley P., Approaches to conceptual clustering, Proc. 9 IJCAI, Los Angeles, 1985, pp. 691–697

    Google Scholar 

  12. Fisher D., Knowledge acquisition via incremental conceptual clustering, Machine Learning 2,2,1987, pp. 139–172

    Google Scholar 

  13. Lebowitz M., Experiments with incremental concept formation: UNIMEM, Machine Learning 2,2, 1987, pp. 103–138

    Google Scholar 

  14. Fisher D.H., Conceptual clustering. learning from examples and inference, Proc. 4 Intenationbal Workshop on Machine Learning, Irvine, Morgan Kaufaman, 1987, pp. 38–49

    Google Scholar 

  15. Hadzikadic M., Yun D.Y.Y., Concept formation by goal-driven context-dependent classification, Proc.3 International Symposium on Methodologies for Intelligent Systems, Toronto, Italy, 1988, pp. 322–332

    Google Scholar 

  16. Kodratoff Y., Tecusi G., Learning based on conceptual distance, IEEE trans. on PAMI-10,6 (1988), pp. 897–909.

    Google Scholar 

  17. Hadzikadic M., Yun D.Y.Y., Concept formation by incremental conceptual clustering, Proc. IJCAI'89 Vol. 2, pp. 831–836

    Google Scholar 

  18. Fisher D.H., Noice-tolerant conceptual clustering, Proc. IJCAI'89 Vol. 2, pp. 825–830

    Google Scholar 

  19. Gennari J.H., Langley P., Fisher D., Models of incremental concept formation, Artificial Intelligence 40 (1989) 11–61

    Google Scholar 

  20. S.T. Wierzchon, A. Pacan, M.A. Klopotek, An Object-oriented Representation Framework For Hierarchical Evidential Reasoning, in: Ph. Jorrand, V. Sgurev: “Artificial Intelligence IV: Methods, Systems And Applications”, Proc. Conf.AIMSA '90, Albena, Bulgaria, 19–22 Sept.1990 r, North Holland, Amsterdam, New York, Oxford, Tokyo, 1990, pp. 239–248

    Google Scholar 

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Authors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, P.O.Box 22, 00-901, Warsaw, Poland

    Mieczyslaw A. Klopotek

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  1. Mieczyslaw A. Klopotek
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Philippe Jorrand Jozef Kelemen

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© 1991 Springer-Verlag Berlin Heidelberg

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Klopotek, M.A. (1991). On the phenomenon of flattening “flexible prediction” concept hierarchy. In: Jorrand, P., Kelemen, J. (eds) Fundamentals of Artificial Intelligence Research. FAIR 1991. Lecture Notes in Computer Science, vol 535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54507-7_9

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  • DOI: https://doi.org/10.1007/3-540-54507-7_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54507-1

  • Online ISBN: 978-3-540-38420-5

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