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Machine Learning

, Volume 2, Issue 2, pp 139–172 | Cite as

Knowledge acquisition via incremental conceptual clustering

  • Douglas H. Fisher
Article

Abstract

Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.

Keywords

Conceptual clustering concept formation incremental learning inference hill climbing 

References

  1. Brachman, R. J. (1985). I lied about the trees. AI Magazine, 6, 80–93.Google Scholar
  2. Carbonell, J. G., & Hood, G. (1986). The World Modelers Project: Objectives and simulator architecture. In R. S.Michalski, J. G.Carbonell, & T. M.Mitchell (Eds.), Machine learning: A guide to current research. Boston, MA: Kluwer.Google Scholar
  3. Cheeseman, P. (1985). In defense of probability. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 1002–1009). Los Angeles, CA: Morgan Kaufmann.Google Scholar
  4. Cheng, Y., & Fu, K. (1985). Conceptual clustering in knowledge organization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 592–598.Google Scholar
  5. Clancey, W. J. (1984). Classification problem solving. Proceedings of the National Conference on Artificial Intelligence (pp. 49–55). Austin, TX: Morgan Kaufmann.Google Scholar
  6. Dietterich, T. G. (1982). Learning and inductive inference. In P. R.Cohen & E. A.Feigenbaum (Eds.), The handbook of artificial intelligence. Los Altos, CA: Morgan Kaufmann.Google Scholar
  7. Dietterich, T. G., & Michalski, R. S. (1983). A comparative review of selected methods of learning from examples. In R. S.Michalski, J. G.Carbonell, & T. M.Mitchell (Eds.), Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann.Google Scholar
  8. Everitt, B. (1980). Cluster analysis. London: Heinemann Educational Books.Google Scholar
  9. Feigenbaum, E. A., & Simon, H. A. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8, 305–336.Google Scholar
  10. Fisher, D. H. (1985). A hierarchical conceptual clustering algorithm (Technical Report 85–21). Irvine, CA: University of California, Department of Information and Computer Science.Google Scholar
  11. Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Doctoral dissertation, Department of Information and Computer Science, University of California, Irvine.Google Scholar
  12. Fisher, D. H., & Langley, P. (1985). Approaches to conceptual clustering. Proceedings of the Ninth International Conference on Artificial Intelligence (pp. 691–697). Los Angeles, CA: Morgan Kaufmann.Google Scholar
  13. Fisher, D., & Langley, P. (1986). Methods of conceptual clustering and their relation to numerical taxonomy. In W.Gale (Ed.), Artificial intelligence and statistics. Reading, MA: Addison-Wesley.Google Scholar
  14. Fu, L., & Buchanan, B. G. (1985). Learning intermediate concepts in constructing a hierarchical knowledge base. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 659–666). Los Angeles, CA: Morgan Kaufmann.Google Scholar
  15. Gennari, J. H., Langley, P., & Fisher, D. H. (1987). Models of incremental concept formation (Technical Report). Irvine, CA: University of California, Department of Information and Computer Science.Google Scholar
  16. Gluck, M. A., & Corter, J. E. (1985). Information, uncertainty, and the utility of categories. Proceedings of the Seventh Annual Conference of the Cognitive Science Society (pp. 283–287). Irvine, CA: Lawrence Erlbaum Associates.Google Scholar
  17. Hanson, S. J., & Bauer, M. (1986). Machine learning, clustering, and polymorphy. In L. N.Kanal & J. F.Lemmer (Eds.), Uncertainty in artificial intelligence. Amsterdam: North-Holland.Google Scholar
  18. Kolodner, J. L. (1983). Reconstructive memory: A computer model. Cognitive Science, 7, 281–328.Google Scholar
  19. Langley, P., & Carbonell, J. G. (1984). Approaches to machine learning. Journal of the American Society for Information Science, 35, 306–316.Google Scholar
  20. Langley, P., Kibler, D., & Granger, R. (1986). Components of learning in a reactive environment. In R. S.Michalski, J. G.Carbonell, & T. M.Mitchell (Eds.), Machine learning: A guide to current research. Boston, MA: Kluwer.Google Scholar
  21. Langley, P., & Sage, S. (1984). Conceptual clustering as discrimination learning. Proceedings of the Fifth Biennial Conference of the Canadian Society for Computational Studies of Intelligence (pp. 95–98). London, Ontario, Canada.Google Scholar
  22. Lebowitz, M. (1982). Correcting erroneous generalizations. Cognition and Brain Theory, 5, 367–381.Google Scholar
  23. Lebowitz, M. (1986a). Concept learning in a rich input domain: Generalization-based memory. In R. S.Michalski, J. G.Carbonell, & T. M.Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 2). Los Altos, CA: Morgan Kaufmann.Google Scholar
  24. Lebowitz, M. (1986b). Integrated learning: Controlling explanation. Cognitive Science, 10, 219–240.Google Scholar
  25. Medin, D. L., Wattenmaker, W. D., & Michalski, R. S. (1986). Constraints and preferences in inductive learning: An experimental study comparing human and machine performance (Technical Report ISG 86–1). Urbana, IL: University of Illinois, Department of Computer Science.Google Scholar
  26. Mervis, C. B., & Rosch, E. (1981). Categorization of natural objects. Annual Review of Psychology, 32, 89–115.Google Scholar
  27. Michalski, R. S. (1980). Knowledge acquisition through conceptual clustering: A theoretical framework and algorithm for partitioning data into conjunctive concepts. International Journal of Policy Analysis and Information Systems, 4, 219–243.Google Scholar
  28. Michalski, R. S., & Stepp, R. E. (1983). Learning from observation: Conceptual clustering. In R. S.Michalski, J. G.Carbonell, & T. M.Mitchell (Eds.), Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann.Google Scholar
  29. Mitchell, T. M. (1982). Generalization as search. Artificial Intelligence, 18, 203–226.Google Scholar
  30. Pearl, J. (1985). Learning hidden causes from empirical data. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 567–572). Los Angeles, CA: Morgan Kaufmann.Google Scholar
  31. Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. In R. S.Michalski, J. G.Carbonell, & T. M.Mitchell (Eds.), Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann.Google Scholar
  32. Reinke, R., & Michalski, R. S. (1986). Incremental learning of concept descriptions. Machine intelligence (Vol. 11). Oxford University Press.Google Scholar
  33. Rendell, L. (1986). A general framework for induction and a study of selective induction. Machine Learning, 1, 177–226.Google Scholar
  34. Sammut, C., & Hume, D. (1986). Learning concepts in a complex robot world. In R. S.Michalski, J. G.Carbonell, & T. M.Mitchell (Eds.), Machine learning: A guide to current research. Boston, MA: Kluwer.Google Scholar
  35. Schlimmer, J. C., & Fisher, D. H. (1986). A case study of incremental concept induction. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 496–501). Philadelphia, PA: Morgan Kaufmann.Google Scholar
  36. Schlimmer, J. C., & Granger, R. H. (1986). Beyond incremental processing: Tracking concept drift. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 502–507). Philadelphia, PA: Morgan Kaufmann.Google Scholar
  37. Simon, H. A. (1969). The sciences of the artificial. Cambridge, MA: MIT Press.Google Scholar
  38. Smith, E. E., & Medin, D. L. (1981). Categories and concepts. Cambridge, MA: Harvard University Press.Google Scholar
  39. Stepp, R. E. (1984). Conjunctive conceptual clustering: A methodology and experimentation (Technical Report UIUCDCS-R-84-1189). Doctoral dissertation, Department of Computer Science, University of Illinois, Urbana.Google Scholar
  40. Stepp, R. E., & Michalski, R. S. (1986). Conceptual clustering: Inventing goal-directed classifications of structured objects. In R. S.Michalski, J. G.Carbonell, & T. M.Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 2). Los Altos, CA: Morgan Kaufmann.Google Scholar
  41. Vere, S. A. (1978). Inductive learning of relational productions. In D.Waterman & F.Hayes-Roth (Eds.), Pattern-directed inference systems. Orlando, FL: Academic Press.Google Scholar
  42. Winston, P. H. (1975). Learning structural descriptions from examples. In P. H.Winston (Ed.), The psychology of computer vision. New York: McGraw-Hill.Google Scholar

Copyright information

© Kluwer Academic Publishers 1987

Authors and Affiliations

  • Douglas H. Fisher
    • 1
  1. 1.Irvine Computational Intelligence Project, Department of Information and Computer ScienceUniversity of CaliforniaIrvineU.S.A.

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