Conceptual clustering
Conceptual clustering is a machine learning paradigm for unsupervised classification. It is distinguished from ordinary data clustering by generating a concept description for each generated class. Most methods are capable of generating hierarchical category structures. See Categorization for more information on hierarchy. Conceptual clustering is closely related to formal concept analysis, FCA.
Algorithms
A fair number of algorithms have been proposed for conceptual clustering. Some examples are given below:
- Talavera & Béjar 2001
- CLUSTER/2 (Michalski & Stepp 1983)
- WITT (Hanson & Bauer 1989),
- UNIMEM (Lebowitz 1987)
- COBWEB (Fisher 1987)
- ITERATE (Biswas, Weinberg & Fisher 1998),
- GALOIS (Carpineto & Romano 1993),
- SUBDUE (Jonyer, Cook & Holder 2001).
More general discussions and reviews of conceptual clustering:
- Michalski 1980
- Gennari, Langley, & Fisher 1989
- Fisher & Pazzani 1991
- Fisher & Langley 1986
- Stepp & Michalski 1986
References
Biswas, G., Weinberg, J. B. & Fisher, D. H. (1998), Iterate: A conceptual clustering algorithm for data mining, IEEE Transactions on Systems, Man, and Cybernetics 28 part C, 100-111.
Carpineto, C. & Romano, G. (1993), Galois: An order-theoretic approach to conceptual clustering, in Proceedings of 10th International Conference on Machine Learning, Amherst, pp. 33-40.
Fisher, D. H. (1987), Knowledge acquisition via incremental conceptual clustering, Machine Learning 2, 139-172.
Fisher, D. & Langley, P. (1986), Conceptual clustering and its relation to numerical taxonomy, in W. A. Gale, ed., Arti cial Intelligence and Statistics, Addison-Wesley, Reading, MA, pp. 77-116.
Fisher, D. & Pazzani, M. (1991), Computational models of concept learning, in D. H. Fisher, M. J. Pazzani & P. Langley, eds, Concept Formation: Knowledge and Experience in Unsupervised Learning, Morgan Kaufmann, San Mateo, CA, chapter 1, pp. 343.
Gennari, J. H., Langley, P. & Fisher, D. (1989), Models of incremental concept formation, Arti cial Intelligence 40, 11-61.
Hanson, S. J. & Bauer, M. (1989), Conceptual clustering, categorization, and polymorphy, Machine Learning 3, 343-372
Jonyer, I., Cook, D. J. & Holder, L. B. (2001), Graph-based hierarchical conceptual clustering, Journal of Machine Learning Research 2, 1943.
Lebowitz, M. (1987), Experiments with incremental concept formation, Machine Learning 2, 103138.
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. & Stepp, R. E. (1983), Learning from observation: Conceptual clustering, in R. S. Michalski, J. G. Carbonell & T. M. Mitchell, eds, Machine Learning: An Arti cial Intelligence Approach, Tioga, Palo Alto, CA, pp. 331-363.
Stepp, R. E. & Michalski, R. S. (1986), Conceptual clustering: Inventing goal-oriented classi cations of structured objects, in R. S. Michalski, J. G. Carbonell & T. M. Mitchell, eds, Machine Learning: An Arti cial Intelligence Approach, Morgan Kaufmann, Los Altos, CA, pp. 471-498.
Talavera, L. & Béjar, J. (2001), Generality-based conceptual clustering with probabilistic concepts, IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 196-206.