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.

Conceptual clustering concept formation incremental learning inference hill climbing 

References

  1. Brachman, R. J.(1985).I lied about the trees,AI Magazine,6, 80-93Google 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: KluwerGoogle 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 KaufmannGoogle Scholar
  4. Cheng,Y., & Fu, K. (1985).Conceptual clustering in knowledge organization,IEEE Transactions on Pattern Analysis and Machine Intelli-gence,7,592-598Google Scholar
  5. Clancey, W. J. (1984).Classification problem solving,Proceedings of the National Conference on Artificial Intelligence(pp,49-55).Austin,TX: Morgan KaufmannGoogle 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 KaufmannGoogle 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 KaufmannGoogle Scholar
  8. Everitt, B. (1980).Cluster analysis,London: Heinemann Educational BooksGoogle Scholar
  9. Feigenbaum, E. A., & Simon, H. A. (1984).EPAM-like models of recogni-tion and learning,Cognitive Science,8, 305-336Google 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 ScienceGoogle Scholar
  11. Fisher, D. H. (1987).Knowledge acquisition via incremental conceptual clustering,Doctoral dissertation,Department of Information and Computer Science,University of California, IrvineGoogle 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 KaufmannGoogle 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-WesleyGoogle 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 KaufmannGoogle Scholar
  15. Gennari, J. H., Langley, P., & Fisher, D. H. (1987).Models of incremen-tal concept formation(Technical Report).Irvine,CA: University of California,Department of Information and Computer ScienceGoogle 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 AssociatesGoogle 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-HollandGoogle Scholar
  18. Kolodner, J. L. (1983).Reconstructive memory:A computer model,Cog-nitive Science,7, 281-328Google Scholar
  19. Langley, P., & Carbonell, J. G. (1984).Approaches to machine learning, Journal of the American Society for Information Science,35, 306-316Google 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: KluwerGoogle 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,CanadaGoogle Scholar
  22. Lebowitz, M. (1982).Correcting erroneous generalizations,Cognition and Brain Theory,5, 367-381Google Scholar
  23. Lebowitz, M. (1986a).Concept learning in a rich input domain:General-ization-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 KaufmannGoogle Scholar
  24. Lebowitz, M. (1986b).Integrated learning:Controlling explanation,Cog-nitive Science,10, 219-240Google 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 ScienceGoogle Scholar
  26. Mervis, C. B., & Rosch, E. (1981).Categorization of natural objects, Annual Review of Psychology,32, 89-115Google Scholar
  27. Michalski, R. S. (1980).Knowledge acquisition through conceptual clus-tering:A theoretical framework and algorithm for partitioning data into conjunctive concepts,International Journal of Policy Analysis and Information Systems,4, 219-243Google Scholar
  28. Michalski, R. S., & Stepp, R. E. (1983).Learning from observation:Con-ceptual clustering,In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.).Machine learning:An artificial intelligence approach,Los Altos,CA: Morgan KaufmannGoogle Scholar
  29. Mitchell, T. M. (1982).Generalization as search,Artificial Intelligence, 18, 203-226Google 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 KaufmannGoogle 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 KaufmannGoogle Scholar
  32. Reinke, R., & Michalski, R. S. (1986).Incremental learning of concept descriptions,Machine intelligence(Vol,11).Oxford University PressGoogle Scholar
  33. Rendell, L. (1986).A general framework for induction and a study of selective induction,Machine Learning,1, 177-226Google 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: KluwerGoogle 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 KaufmannGoogle 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 KaufmannGoogle Scholar
  37. Simon, H. A. (1969).The sciences of the artificial,Cambridge, MA:MIT PressGoogle Scholar
  38. Smith, E. E., & Medin, D. L. (1981).Categories and concepts,Cambridge, MA: Harvard University PressGoogle 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, UrbanaGoogle 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 KaufmannGoogle 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 PressGoogle Scholar
  42. Winston, P. H. (1975).Learning structural descriptions from examples,In P. H. Winston (Ed.).The psychology of computer vision,New York: McGraw-HillGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1987

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  • Douglas H. Fisher

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