Empirical substructure discovery
Proceedings of the sixth international workshop on Machine learning
ML92 Proceedings of the ninth international workshop on Machine learning
Systems for Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
Graph-based hierarchical conceptual clustering
The Journal of Machine Learning Research
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
Concept discovery in structural data requires the identification of repetitive substructures in the data. A method for discovering substructures in data using an inexact graph match is described. An implementation of the authors' SUBDUE system that employs an inexact graph match to discover substructures which occur often in the data, but not always in the same form, is described. This inexact substructure discovery can be used to formulate fuzzy concepts, compress the data description, and discover interesting structures in data that are found either in an identical or in a slightly convoluted form. Examples from the domains of scene analysis and chemical compound analysis demonstrate the benefits of the inexact discovery technique.