Conceptual clustering of structured objects: a goal-oriented approach
Artificial Intelligence
ACM Computing Surveys (CSUR)
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th 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)
Induction of optimal semantic semi-distances for clausal knowledge bases
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Statistical Relational Learning with Formal Ontologies
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Fuzzy Clustering for Semantic Knowledge Bases
Fundamenta Informaticae - Methodologies for Intelligent Systems
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A clustering method is presented which can be applied to relational knowledge bases (e.g. DATALOG deductive databases). It can be used to discover interesting groups of resources through their (semantic) annotations expressed in the standard logic programming languages. The method exploits an 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 a fusion of the classic BISECTING K-MEANS with approaches based on medoids that are typically applied to relational representations. We discuss its complexity and potential applications to several tasks.