Abstract
A multi-relational clustering method is presented which can be applied to complex knowledge bases storing resources expressed in the standard Semantic Web languages. It adopts effective and language-independent dissimilarity measures that are based on a finite number of dimensions corresponding to a committee of discriminating features(represented by concept descriptions). The clustering algorithm expresses the possible clusterings in tuples of central elements (medoids, w.r.t. the given metric) of variable length. It iteratively adjusts these centers following the rationale of fuzzy clustering approach, i.e. one where the membership to each cluster is not deterministic but rather ranges in the unit interval. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices.
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Fanizzi, N., d’Amato, C., Esposito, F. (2009). Fuzzy Clustering for Categorical Spaces. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_19
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DOI: https://doi.org/10.1007/978-3-642-04125-9_19
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