Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Statistical analysis with missing data
Statistical analysis with missing data
Induction and the discovery of the causes of scurvy: a computational reconstruction
Artificial Intelligence - Special issue on scientific discovery
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Tabu Search
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Conceptual Graph Matching for Semantic Search
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
An experimental study of concept formation
An experimental study of concept formation
A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction
Applied Intelligence
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This paper describes a theoretical framework for inducing knowledge from incomplete data sets. The general framework can be used with any formalism based on a lattice structure. It is illustrated within two formalisms: the attribute-value formalism and Sowa's conceptual graphs. The induction engine is based on a non-supervised algorithm called default clustering which uses the concept of stereotype and the new notion of default subsumption, inspired by the default logic theory. A validation using artificial data sets and an application concerning the extraction of stereotypes from newspaper articles are given at the end of the paper.