Models of incremental concept formation
Artificial Intelligence
Incremental concept formation with composite objects
Proceedings of the sixth international workshop on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conceptual clustering in a first order logic representation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
ACM Computing Surveys (CSUR)
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
A Generalization Model Based on OI-implication for Ideal Theory Refinement
Fundamenta Informaticae - Intelligent Systems
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Knowledge extraction represents an important issue that concerns the ability to identify valid, potentially useful and understandable patterns from large data collections. Such a task becomes more difficult if the domain of application cannot be represented by means of an attribute-value representation. Thus, a more powerful representation language, such as First-Order Logic, is necessary. Due to the complexity of handling First-Order Logic formulæ, where the presence of relations causes various portions of one description to be possibly mapped in different ways onto another description, few works presenting techniques for comparing descriptions are available in the literature for this kind of representations. Nevertheless, the ability to assess similarity between first-order descriptions has many applications, ranging from description selection to flexible matching, from instance-based learning to clustering. This paper tackles the case of Conceptual Clustering, where a new approach to similarity evaluation, based on both syntactic and semantic features, is exploited to support the task of grouping together similar items according to their relational description. After presenting a framework for Horn Clauses (including criteria, a function and composition techniques for similarity assessment), classical clustering algorithms are exploited to carry out the grouping task. Experimental results on realworld datasets prove the effectiveness of the proposal.