Abstract
This paper examines the problem of clustering a sequence of objects that cannot be described with a predefined list of attributes (or variables). In many applications, a fixed list of attributes cannot be determined without substantial pre-processing. An extension of the traditional propositional formalism is thus proposed, which allows objects to be represented as a set of components, i.e. there is no mapping between attributes and values. The algorithm used for clustering is briefly illustrated, and mechanisms to handle sets are described. Some empirical evaluations are also provided to assess the validity of the approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
G. Bisson. Conceptual clustering in a first order logic representation. In Proceedings of the Tenth European Conference on Artificial Intelligence, pages 458–462. J. Wiley and Sons, 1992.
E. Diday, J. Lemaire, J. Pouget, and F. Testu. Eléments d’Analyse de Données. Dunod, Paris, 1982.
D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2: 139–172, 1987.
D. H. Fisher, M. J. Pazzani, and P. Langley, editors. Concept Formation: Knowledge and Experience in Unsupervised Learning. Morgan Kaufmann, 1991.
J. H. Gennari, P. Langley, and D. H. Fisher. Models of incremental concept formation. Artificial Intelligence, 40: 11–61, 1989.
J. J. Korczak, D. Blamont, and A. Ketterlin. Thematic image segmentation by a concept formation algorithm. In Proceeding of the European Symposium on Satellite Remote Sensing, 1994.
J-Ü Kietz and K. Morik. A polynomial approach to the constructive induction of structural knowledge. Machine Learning, 14: 193–217, 1994.
J. J. Korczak and M. Rymarczyk. Application of classical clustering methods to digital image analysis. Technical report, Université Louis Pasteur, Strasbourg, France, 1993.
D. Marr and E. Hildreth. Theory of edge detection. Proceedings of the Royal Society of London, B. 207: 187–217, 1980.
R. S. Michalski. A theory and methodology of inductive learning. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann, 1983.
R. S. Michalski and R. E. Stepp. Learning from observation: Conceptual clustering. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann, 1983.
K. Thompson and P. Langley. Concept formation in structured domains. In Fisher et al. [FPL91].
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer-Verlag New York, Inc.
About this chapter
Cite this chapter
Ketterlin, A., Gançarski, P., Korczak, J.J. (1996). Hierarchical Clustering of Composite Objects with a Variable Number of Components. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_22
Download citation
DOI: https://doi.org/10.1007/978-1-4612-2404-4_22
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94736-5
Online ISBN: 978-1-4612-2404-4
eBook Packages: Springer Book Archive