Conceptual clustering of structured objects: a goal-oriented approach
Artificial Intelligence
Algorithms for clustering data
Algorithms for clustering data
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
The Description Logic Handbook
The Description Logic Handbook
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A clustering method is presented which can be applied to relational knowledge bases. It can be used to discover interesting groupings of resources through their (semantic) annotations expressed in the standard languages employed for modeling concepts in the Semantic Web. The method exploits a simple (yet effective and language-independent) semi-distance measure for individuals, that is based on the resource semantics w.r.t. a number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). The algorithm is an fusion of the classic Bisecting k-Meanswith approaches based on medoids since they are intended to be applied to relational representations. We discuss its complexity and the potential applications to a variety of important tasks.