Abstract
In this paper we describeWitt, a computational model of categorization and conceptual clustering that has been motivated and guided by research on human categorization. Properties of categories to which humans are sensitive include best or prototypical members, relative contrasts between categories, and polymorphy (neither necessary nor sufficient feature rules). The system uses pairwise feature correlations to determine the “similarity” between objects and clusters of objects. allowing the system a flexible representation scheme that can model common-feature categories and polymorphous categories. This intercorrelation measure is cast in terms of an information-theoretic evaluation function that directsWitt's search through the space of clusterings. This information-theoretic similarity metric also can be used to explain basic-level and typicality effects that occur in humans.Witt has been tested on both artificial domains and on data from the 1985World Almanac, and we have examined the effect of various system parameters on the quality of the model's behavior.
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Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., & Freeman, D. (1988).Proceedings of the Fifth International Conference on Machine Learning (pp. 54–64). Ann Arbor, MI: Morgan Kaufmann.
DeJong, G., & Mooney, R. (1986). Explanation-based learning: An alternative view.Machine Learning,1, 145–176.
Dennis, I., Hampton, J. A., & Lea, S. E. G. (1973). New problem in concept formation.Nature,243, 101–102.
Estes, W. K. (1986). Memory storage and retrieval processes in category learning.Journal of Experimental Psychology: General,115, 155–174.
Everitt, B. (1974).Cluster analysis. London: Heinemann Educational Books.
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.
Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering.Machine Learning,2, 139–172.
Grossberg, S. (1976). Adaptive pattern classification and universal recoding. Part I: Parallel development and coding of neural feature detectors.Biological Cybernetics,23, 121–134.
Homa, D. (1978). Abstraction of ill-defined form.Journal of Experimental Psychology: Human Learning and Memory,4, 407–416.
Lance, G. N., & Williams, W. T. (1967). Note on a new information-statistic classificatory program.Computer Journal,9, 373–380.
Lebowitz, M. (1987). Experiments with incremental concept formation:Unimem.Machine Learning,2, 103–138.
Medin, D. L., Wattenmaker, W. D., & Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness and category construction.Cognitive Psychology,19, 242–279.
Michalski, R. S. (1980). Knowledge acquisition through conceptual clustering: A theoretical framework and an algorithm for partitioning data into conjunctive concepts.International Journal of Policy Analysis and Information Systems,4, 219–244.
Michalski, R. S., & Stepp, R. E. (1983a). 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.
Michalski, R. S., & Stepp, R. E. (1983b). Automated construction of classifications: Conceptual clustering verses numerical taxonomy.IEEE Transactions on Pattern Analysis and Machine Intelligence,5, 396–410.
Miller, G. A. (1971). Empirical methods in the study of semantics. In D. D., Steinberg & L. A., Jakobovits (Eds.),Semantics. Cambridge: Cambridge University Press.
Mitchell, T. M. (1978).Version spaces: An approach to concept learning. Doctoral dissertation, Department of Electrical Engineering, Stanford University, Palo Alto, CA.
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986). Explanation-based generalization: A unifying view.Machine Learning,1, 47–80.
Murphy, G. L. (1982). Cue validity and levels of categorization.Psychological Bulletin,91, 174–177.
Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence.Psychological Review,92, 289–316.
Orloci, L. (1969). Information analysis of structure in biological collections.Nature,223, 483–484.
Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas.Journal of Experimental Psychology,77, 353–363.
Quinlan, J. R. (1986). Induction of decision trees.Machine Learning,1, 81–106.
Rosch, E., Mervis, C., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories.Cognitive Psychology,7, 382–439.
Rosch, E., & Lloyd, B. B. (Eds.). (1978).Cognition and categorization. Hillsdale, NJ: Lawrence Erlbaum.
Rumelhart, D. E., & McClelland, J. L. (1986).Parallel distributed processing: Explorations in the micro-structure of cognition. Cambridge, MA: MIT Press.
Schank, R. C., Collins, G. C., & Hunter, L. E. (1986). Transcending inductive category formation in learning.Behavioral and Brain Sciences,9, 639–686.
Smith, E. E., & Medin, D. L. (1981).Categories and concepts, Cambridge, MA: Harvard University Press.
Sneath, P. H., & Sokal, R. R. (1973).Numerical taxonomy: The principles and practice of numerical classification. San Francisco, CA: Freeman.
Wallace, C. S., & Boulton, D. M. (1968). An information measure for classification.Computer Journal,11, 185–194.
Winston, P. H. (1975). Learning structural descriptions from examples. In P. H., Winston (Ed.),The psychology of computer vision. New York: McGraw-Hill.
Wittgenstein, L. (1953).Philosophical investigations. Oxford: Basil Blackwell.
Zadeh, L. A. (1965). Fuzzy sets.Information and Control,8, 338–353.
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Hanson, S.J., Bauer, M. Conceptual clustering, categorization, and polymorphy. Mach Learn 3, 343–372 (1989). https://doi.org/10.1007/BF00116838
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DOI: https://doi.org/10.1007/BF00116838