Conceptual clustering

From Wikipedia, the free encyclopedia

You have new messages (last change).

Conceptual clustering is a machine learning paradigm for unsupervised classification developed mainly during the 1980's. It is distinguished from ordinary data clustering by generating a concept description for each generated class. Most conceptual clustering 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.

In conceptual clustering it is not only the inherent structure of the data that drives cluster formation, but also the description language which is available to the learner. Thus, a statistically strong grouping in the data may fail to be extracted by the learner if the prevailing concept description language is incapable of describing that particular regularity. In most implementations, the description language has been limited to feature conjunction.

[edit] Basic Conceptual Clustering Algorithm (COBWEB)

To be added soon.....

[edit] Published Algorithms

A fair number of algorithms have been proposed for conceptual clustering. Some examples are given below:

  • CLUSTER/2 (Michalski & Stepp 1983)
  • COBWEB (Fisher 1987)
  • CYRUS (Kolodner 1983)
  • GALOIS (Carpineto & Romano 1993),
  • GCF (Talavera & Béjar 2001)
  • INC (Hadzikadic & Yun 1989)
  • ITERATE (Biswas, Weinberg & Fisher 1998),
  • LABYRINTH (Thompson & Langley 1989)
  • SUBDUE (Jonyer, Cook & Holder 2001).
  • UNIMEM (Lebowitz 1987)
  • WITT (Hanson & Bauer 1989),

More general discussions and reviews of conceptual clustering can be found in the following publications:

  • Michalski 1980
  • Gennari, Langley, & Fisher 1989
  • Fisher & Pazzani 1991
  • Fisher & Langley 1986
  • Stepp & Michalski 1986

[edit] References

  • Biswas, G.; Weinberg, J. B.; Fisher, Douglas H. (1998). "Iterate: A conceptual clustering algorithm for data mining". IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 28: 100–111.
  • Carpineto, C.; Romano, G. (1993). "Galois: An order-theoretic approach to conceptual clustering". Proceedings of 10th International Conference on Machine Learning, Amherst, 33–40.
  • Fisher, Douglas H. (1987). "Knowledge acquisition via incremental conceptual clustering". Machine Learning 2: 139–172.
  • Fisher, Douglas H.; Langley, Patrick W. (1986). Gale, W. A. (Ed.) "Conceptual clustering and its relation to numerical taxonomy". Artificial Intelligence and Statistics, 77–116, Reading, MA: Addison-Wesley.
  • Fisher, Douglas H.; Pazzani, Michael J. (1991). Fisher, D. H.; Pazzani, M. J.; Langley, P. (Eds.) "Computational models of concept learning". Concept Formation: Knowledge and Experience in Unsupervised Learning, 3–43, San Mateo, CA: Morgan Kaufmann.
  • Gennari, John H.; Langley, Patrick W.; Fisher, Douglas H. (1989). "Models of incremental concept formation". Artificial 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: 19–43.
  • Lebowitz, M. (1987). "Experiments with incremental concept formation". Machine Learning 2: 103–138.
  • 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. (1983). Michalski, R. S.; Carbonell, J. G.; Mitchell, T. M. (Eds.) "Learning from observation: Conceptual clustering". Machine Learning: An Artificial Intelligence Approach, 331–363, Palo Alto, CA: Tioga.
  • Stepp, R. E.; Michalski, R. S. (1986). Michalski, R. S.; Carbonell, J. G.; Mitchell, T. M. (Eds.) "Conceptual clustering: Inventing goal-oriented classifications of structured objects". Machine Learning: An Artificial Intelligence Approach, 471–498, Los Altos, CA: Morgan Kaufmann.
  • Talavera, L.; Béjar, J. (2001). "Generality-based conceptual clustering with probabilistic concepts". IEEE Transactions on Pattern Analysis and Machine Intelligence 23: 196–206.
Retrieved from "http://en.wikipedia.org../../../c/o/n/Conceptual_clustering.html"