Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Models of incremental concept formation
Artificial Intelligence
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
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
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
Building Concept Lattices by Learning Concepts from RDF Graphs Annotating Web Documents
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
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The goal of conceptual clustering is to build a set of embedded classes, which cluster objects based on their similarities. Knowledge organization aims at generating the set of most specific classes: the Generalization Space. It has applications in the field of data mining, knowledge indexation or knowledge acquisition. Efficient algorithms have been proposed for data described in 〈attribute, value〉 pairs formalism and for taking into account domain knowledge. Our research focuses on the organization of relational knowledge represented using conceptual graphs. In order to avoid the combinatorial explosion due to the relations in the building of the Generalization Space, we progressively introduce the complexity of the relations. The KIDS algorithm is based upon an iterative data reformulation which allows us to use an efficient propositional knowledge organization algorithm. Experiments show that the KIDS algorithm builds an organization of relational concepts but remains with a complexity that grows linearly with the number of considered objects.