A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
ACM Computing Surveys (CSUR)
Propositionalization approaches to relational data mining
Relational Data Mining
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Relational Distance-Based Clustering
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Analogical Reasoning in Description Logics
Uncertainty Reasoning for the Semantic Web I
Conceptual clustering and its application to concept drift and novelty detection
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.