Conceptual clustering of structured objects: a goal-oriented approach
Artificial Intelligence
Models of incremental concept formation
Artificial Intelligence
Logic programming and databases
Logic programming and databases
Attributive concept descriptions with complements
Artificial Intelligence
Concept formation in structured domains
Concept formation knowledge and experience in unsupervised learning
Conceptual clustering in a first order logic representation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
{\cal A}{\cal L}-log: Integrating Datalog and Description Logics
Journal of Intelligent Information Systems
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Conceptual Clustering of Complex Objects: A Generalization Space based Approach
ICCS '95 Proceedings of the Third International Conference on Conceptual Structures: Applications, Implementation and Theory
Clustering Ontology-Based Metadata in the Semantic Web
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Hierarchical Conceptual Clustering in a First Order Representation
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
On the Missing Link Between Frequent Pattern Discovery and Concept Formation
Inductive Logic Programming
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This paper presents a novel approach to Conceptual Clustering in First Order Logic (FOL) which is based on the assumption that candidate clusters can be obtained by looking for frequent association patterns in data. The resulting method extends therefore the levelwise search method for frequent pattern discovery. It is guided by a reference concept to be refined and returns a directed acyclic graph of conceptual clusters, possibly overlapping, that are subconcepts of the reference one. The FOL fragment chosen is $\mathcal{AL}$-log, a hybrid language that merges the description logic $\mathcal{ALC}$ and the clausal logic Datalog. It allows the method to deal with both structural and relational data in a uniform manner and describe clusters determined by non-hierarchical relations between the reference concept and other concepts also occurring in the data. Preliminary results have been obtained on Datalog data extracted from the on-line CIA World Fact Book and enriched with a $\mathcal{ALC}$ knowledge base.