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.
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© 1996 Springer-Verlag New York, Inc.
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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
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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
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