Hierarchical Clustering of Composite Objects with a Variable Number of Components
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:303-309, 1995.
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, such a crisp representation cannot be determined. An extension of the traditionnal propositionnal formalism is thus proposed, which allows objects to be represented as a set of components. 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.