References

Ahn, W. & Medin, D. L. (1989). A two-stage categorization model of family resemblance sorting. In Proceedings of the Eleventh Annual Conference of the Cognitive Science Society, 315--322. Ann Arbor, MI: Lawrence Erlbaum.

Anderson, J. R. & Matessa, M. (1991). An iterative Bayesian algorithm for categorization. In Fisher, D., Pazzani, M., & Langley, P., Concept formation: Knowledge and Experience in Unsupervised Learning. San Mateo, CA: Morgan Kaufmann.

Biswas, G., Weinberg, J., & Li, C. (1994). ITERATE: A conceptual clustering method for knowledge discovery in databases. In Braunschweig, B. & Day, R., Innovative Applications of Artificial Intelligence in the Oil and Gas Industry. Editions Technip.

Biswas, G., Weinberg, J. B., Yang, Q., & Koller, G. R. (1991). Conceptual clustering and exploratory data analysis. In Proceedings of the Eighth International Machine Learning Workshop, 591--595. San Mateo, CA: Morgan Kaufmann.

Carpineto, C. & Romano, G. (1993). GALOIS: An order-theoretic approach to conceptual clustering. In Proceedings of the Tenth International Conference on Machine Learning, 33--40. Amherst, MA: Morgan Kaufmann.

Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., & Freeman, D. (1988). AUTOCLASS: A Bayesian classification system. In Proceedings of the Fifth International Machine Learning Conference, 54--64. Ann Arbor, MI: Morgan Kaufmann.

Corter, J. & Gluck, M. (1992). Explaining basic categories: feature predictability and information. Psychological Bulletin, 111, 291--303.

De Alte Da Veiga, F. (1994). Data Analysis in Biomedical Research: A Novel Methodological Approach and its Implementation as a Conceptual Clustering Algorithm (in Portuguese). Ph.D. thesis, Universidade de Coimbra, Unidade de Biomatemática e Informática Médica da Faculdade de Medicina.

Decaestecker, C. (1991). Description contrasting in incremental concept formation. In Kodratoff, Y., Machine Learning -- EWSL-91, No. 482, Lecture Notes in Artificial Intelligence, 220--233. Springer-Verlag.

Devaney, M. & Ram, A. (1993). Personal communication, oct. 1993.

Duda, R. O. & Hart, P. E. (1973). Pattern Classification and Scene Analysis. New York, NY: Wiley and Sons.

Everitt, B. (1981). Cluster Analysis. London: Heinemann.

Fayyad, U. (1991). On the Induction of Decision Trees for Multiple Concept Learning. Ph.D. thesis, University of Michigan, Ann Arbor, MI: Department of Computer Science and Engineering.

Fisher, D. (1995). Optimization and simplification of hierarchical clusterings. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, 118--123. Menlo Park, CA: AAAI Press.

Fisher, D. & Hapanyengwi, G. (1993). Database management and analysis tools of machine induction. Journal of Intelligent Information Systems, 2, 5--38.

Fisher, D., Xu, L., Carnes, J., Reich, Y., Fenves, S., Chen, J., Shiavi, R., Biswas, G., & Weinberg, J. (1993). Applying AI clustering to engineering tasks. IEEE Expert, 8, 51--60.

Fisher, D., Xu, L., & Zard, N. (1992). Ordering effects in clustering. In Proceedings of the Ninth International Conference on Machine Learning, 163--168. San Mateo, CA: Morgan Kaufmann.

Fisher, D. H. (1987a). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139--172.

Fisher, D. H. (1987b). Knowledge Acquisition via Incremental Conceptual Clustering. Ph.D. thesis, University of California, Irvine, CA: Department of Information and Computer Science.

Fisher, D. H. (1989). Noise-tolerant conceptual clustering. In Proceedings of the International Joint Conference Artificial Intelligence, 825--830. Detroit, MI: Morgan Kaufmann.

Fisher, D. H. & Langley, P. (1990). The structure and formation of natural categories. In Bower, G. H., The Psychology of Learning and Motivation, 25. San Diego, CA: Academic Press.

Fisher, D. H. & Schlimmer, J. (1988). Concept simplification and prediction accuracy. In Proceedings of the Fifth International Conference on Machine Learning, 22--28. Ann Arbor, MI: Morgan Kaufmann.

Gennari, J. (1989). Focused concept formation. In Proceedings of the Sixth International Workshop on Machine Learning, 379--382. San Mateo, CA: Morgan Kaufmann.

Gennari, J., Langley, P., & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11--62.

Gluck, M. A. & Corter, J. E. (1985). Information, uncertainty, and the utility of categories. In Proceedings of the Seventh Annual Conference of the Cognitive Science Society, 283--287. Hillsdale, NJ: Lawrence Erlbaum.

Hadzikadic, M. & Yun, D. (1989). Concept formation by incremental conceptual clustering. In Proceedings of the International Joint Conference Artificial Intelligence, 831--836. San Mateo, CA: Morgan Kaufmann.

Hanson, R., Stutz, J., & Cheeseman, P. (1991). Bayesian classification with correlation and inheritance. In Proceedings of the 12th International Joint Conference on Artificial Intelligence, 692--698. San Mateo, CA: Morgan Kaufmann.

Iba, G. (1989). A heuristic approach to the discovery of macro operators. Machine Learning, 3, 285--317.

Iba, W. & Gennari, J. (1991). Learning to recognize movements. In Fisher, D., Pazzani, M., & Langley, P., Concept Formation: Knowledge and Experience in Unsupervised Learning. San Mateo, CA: Morgan Kaufmann.

Ketterlin, A., Gançarski, P., & Korczak, J. (1995). Hierarchical clustering of composite objects with a variable number of components. In Preliminary papers of the Fifth International Workshop on Artificial Intelligence and Statistics, 303--309.

Kilander, F. (1994). Incremental Conceptual Clustering in an On-Line Application. Ph.D. thesis, Stockholm University, Stockholm, Sweden: Department of Computer and Systems Sciences.

Kolodner, J. L. (1983). Reconstructive memory: A computer model. Cognitive Science, 7, 281--328.

Lebowitz, M. (1982). Correcting erroneous generalizations. Cognition and Brain Theory, 5, 367--381.

Lebowitz, M. (1987). Experiments with incremental concept formation: UNIMEM. Machine Learning, 2, 103--138.

Levinson, R. (1984). A self-organizing retrieval system for graphs. In Proceedings of the National Conference on Artificial Intelligence, 203--206. San Mateo, CA: Morgan Kaufmann.

Lopez de Mantaras, R. (1991). A distance-based attribute selection measure for decision tree induction. Machine Learning, 6, 81--92.

Martin, J. & Billman, D. (1994). Acquiring and combining overlapping concepts. Machine Learning, 16, 121--155.

McKusick, K. & Langley, P. (1991). Constraints on tree structure in concept formation. In Proceedings of the International Joint Conference on Artificial Intelligence, 810--816. San Mateo, CA: Morgan Kaufmann.

McKusick, K. & Thompson, K. (1990). COBWEB/3: A portable implementation (Tech. Rep. No. FIA-90-6-18-2). Moffett Field, CA: AI Research Branch, NASA Ames Research Center.

Medin, D. (1983). Structural principles of categorization. In Tighe, T. & Shepp, B., Perception, Cognition, and Development, 203--230. Hillsdale, NJ: Lawrence Erlbaum.

Michalski, R. S. & Stepp, R. (1983a). Automated construction of classifications: conceptual clustering versus numerical taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5, 219--243.

Michalski, R. S. & Stepp, R. (1983b). Learning from observation: conceptual clustering. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M., Machine Learning: An Artificial Intelligence Approach. San Mateo, CA: Morgan Kaufmann.

Mingers, J. (1989a). An empirical comparison of selection measures for decision-tree induction. Machine Learning, 3, 319--342.

Mingers, J. (1989b). An empirical comparison of pruning methods for decision-tree induction. Machine Learning, 4, 227--243.

Nevins, A. J. (1995). A branch and bound incremental conceptual clusterer. Machine Learning, 18, 5--22.

Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81--106.

Quinlan, J. R. (1987). Simplifying decision trees. International Journal of Man-machine Studies, 27, 221--234.

Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.

Reich, Y. & Fenves, S. (1991). The formation and use of abstract concepts in design. In Fisher, D., Pazzani, M., & Langley, P., Concept Formation: Knowledge and Experience in Unsupervised Learning. San Mateo, CA: Morgan Kaufmann.

Utgoff, P. (1994). An improved algorithm for incremental induction of decision trees. In Proceedings of the Eleventh International Conference on Machine Learning, 318--325. San Mateo, CA: Morgan Kaufmann.

Wallace, C. S. & Dowe, D. L. (1994). Intrinsic classification by MML - the SNOB program. In Proceedings of the 7th Australian Joint Conference on Artificial Intelligence, 37--44. UNE, Armidale, NSW, Australia: World Scientific.

Weiss, S. & Kulikowski, C. (1991). Computer Systems that Learn. San Mateo, CA: Morgan Kaufmann.

Wilcox, C. S. & Levinson, R. A. (1986). A self-organized knowledge base for recall, design, and discovery in organic chemistry. In Pierce, T. H. Hohne, B. A., Artificial Intelligence Applications in Chemistry. Washington, DC: American Chemical Society.