Abstract
A key issue in learning from observations is to build a classification of given objects or situations. Conceptual clustering methods address this problem of recognizing regularities among a set of objects that have not been pre-classified, so as to organize them into a hierarchy of concepts. Early approaches have been limited to unstructured domains, in which objects are described by fixed sets of attribute-value pairs. Recent approaches in structured domains use a first order logic based representation to represent complex objects. The problem addressed in this paper is to provide a basis for the analysis of complex objects clustering represented using conceptual graphs formalism. We propose a new clustering method that extracts a hierarchical categorization of the provided objects from an explicit space of concepts hierarchies, called Generalization Space. We give a general algorithm and expose several complexity factors. This algorithm has been implemented in a system called coing. We provide some empirical results on its use to cluster a large database of Chinese characters.
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Bournaud, I., Ganascia, JG. (1995). Conceptual clustering of complex objects: A generalization space based approach. In: Ellis, G., Levinson, R., Rich, W., Sowa, J.F. (eds) Conceptual Structures: Applications, Implementation and Theory. ICCS 1995. Lecture Notes in Computer Science, vol 954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60161-9_37
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DOI: https://doi.org/10.1007/3-540-60161-9_37
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