Generality-based conceptual clustering with probabilistic concepts

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 23, Issue 2, 2001, Pages 196-206

  Talavera, Luis ^a   Béjar, Javier ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

HIERARCHICAL SYSTEMS; INTERACTIVE COMPUTER SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILISTIC LOGICS; PROBLEM SOLVING;

CONCEPTUAL CLUSTERING; HIERARCHICAL CLUSTERING;

FEATURE EXTRACTION;

EID: 0035249751     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.908969     Document Type: Article

References (15)

1. C. Blake, E. Keogh, and C.J. Merz, UCI Repository of Machine Learning Databases, Univ. of California, Irvine, Dept. of Information and Computer Sciences, 1998. http://www.ics.uci.edu/ -mlearn/MLRep ository.html.
2. D. Fisher, L. Xu, J. Carnes, Y. Reich, S. Fenves, J. Chen, R. Shiavi, G. Biswas, and J. Weinberg, Applying AI Clustering to Engineering Tasks, IEEE Expert, vol. 8, pp. 51-60, 1993.
3. D.H. Fisher, Knowledge Acquisition via Incremental Conceptual Clustering, Machine Learning, no. 2, pp. 139-172, 1987.
4. D.H. Fisher, Iterative Optimization and Simplification of Hierarchical Clusterings, J. Artificial Intelligence Research, vol. 4, pp. 147-179, 1996.
5. D.H. Fisher and M.J. Pazzani, Computational Models of Concept Learning, Concept Formation: Knowledge and Experience in Unsupervised Learning, D.H. Fisher, M.J. Pazzani, and P. Langley, eds. pp. 3-43, San Mateo, Calif.: Morgan Kauffmann, 1991.
6. S.J. Hanson and M. Bauer, Conceptual Clustering, Categorization, and Polymorphy, Machine Learning, no. 3, pp. 343-372, 1989.
7. W. Iba and P. Langley, Unsupervised Learning of Probabilistic Concept Hierarchies, technical report, Inst. for Study of Learning and Expertise, Palo Alto, Calif., 1999.
8. A.K. Jain and R.C. Dubes, Algorithms for Clustering Data. Englewood Cliffs, N.J.: Prentice Hall, 1988.
9. M. Lebowitz, Experiments with Incremental Concept Formation: UNIMEM, Machine Learning, vol. 2, pp. 103-138, 1987.
10. R.S. Michalski and R.E. Stepp, Learning from Observation: Conceptual Clustering, Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, eds, pp. 331-363, San Mateo, Calif.: Morgan Kauffmann, 1983.
11. T.M. Mitchell, Generalization as Search, Artificial Intelligence, vol. 18, pp. 203-226, 1982.
12. G.L. Murphy and E.E. Smith, Basic Level Superiority in Picture Categorization, J. Verbal Learning and Verbal Behavior, vol. 21, pp. 1-20, 1982.
13. Y. Reich and S.V. Barai, Evaluating Machine Learning Models for Engineering Problems, Artificial Intelligence in Eng., vol. 13, no. 3, pp. 257-272, 1999.
14. Y. Reich and S. Fenves, The Formation and Use of Abstract Concepts in Design, Concept Formation: Knowledge and Experience in Unsupervised Learning, D.H. Fisher, M.J. Pazzani, and P. Langley, eds., pp. 323-353, San Mateo, Calif.: Morgan Kauffmann, 1991.
15. E.E. Smith and D.L. Medin, Categories and Concepts. Cambridge, Mass.: Harvard Univ. Press, 1981.