Conceptual clustering of structured objects: a goal-oriented approach
Artificial Intelligence
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
ACM Computing Surveys (CSUR)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Distances and Limits on Herbrand Interpretations
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
The Description Logic Handbook
The Description Logic Handbook
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
Discovering patterns in spatial data using evolutionary programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
OSS: a semantic similarity function based on hierarchical ontologies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A refinement operator based learning algorithm for the ALC description logic
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A clustering method is presented which can be applied to semantically annotated resources in the context of ontological knowledge bases. This method can be used to discover emerging groupings of resources expressed in the standard ontology languages. The method exploits a language-independent semidistance measure over the space of resources, that is based on their semantics w.r.t. a number of dimensions corresponding to a committee of discriminating features represented by concept descriptions. A maximally discriminating group of features can be constructed through a feature construction method based on genetic programming. The evolutionary clustering algorithm proposed is based on the notion of medoids applied to relational representations. It is able to induce a set of clusters by means of a fitness function based on a discernibility criterion. An experimentation with some ontologies proves the feasibility of our method.