Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Models of incremental concept formation
Artificial Intelligence
Pac-learning nondeterminate clauses
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Confirmation-guided discovery of first-order rules with tertius
Machine Learning
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Phase Transitions in Relational Learning
Machine Learning
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Structuring Knowledge Bases Using Automatic Learning
Proceedings of the Sixth International Conference on Data Engineering
Accounting for Domain Knowledge in the Construction of a Generalization Space
ICCS '97 Proceedings of the Fifth International Conference on Conceptual Structures: Fulfilling Peirce's Dream
Structural Machine Learning with Galois Lattice and Graphs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
An application of AI techniques to structuring objects into an optimal conceptual hierarchy
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Approaches to conceptual clustering
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Multistrategy Operators for Relational Learning and Their Cooperation
Fundamenta Informaticae
Multistrategy Operators for Relational Learning and Their Cooperation
Fundamenta Informaticae
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Propositionalization has recently received much attention in the ILP community as a mean to learn efficiently non-determinate concepts using adapted propositional algorithms. This paper proposes to extend such an approach to unsupervised learning from symbolic relational description. To help deal with the known combinatorial explosion of the number of possible clusters and the size of their descriptions, we suggest an approach that gradually increases the expressivity of the relational language used to describe the classes. At each level, only the initial object descriptions that could benefit from such an enriched generalization language are propositionalized. This latter representation allows us to use an efficient propositional clustering algorithm. This approach is implemented in the CAC system. Experiments on a large Chinese character database show the interest of using KIDS to cluster relational descriptions and pinpoint current problems for analyzing relational classifications.