Horn approximations of empirical data
Artificial Intelligence
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Horn axiomatizations for sequential data
Theoretical Computer Science
Mining Frequent Closed Unordered Trees Through Natural Representations
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
An integrated, generic approach to pattern mining: data mining template library
Data Mining and Knowledge Discovery
Prism: An effective approach for frequent sequence mining via prime-block encoding
Journal of Computer and System Sciences
Towards generic pattern mining
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
| Hi-index | 0.00 |
We propose a notion of deterministic association rules for ordered data. We prove that our proposed rules can be formally justified by a purely logical characterization, namely, a natural notion of empirical Horn approximation for ordered data which involves background Horn conditions; these ensure the consistency of the propositional theory obtained with the ordered context. The main proof resorts to a concept lattice model in the framework of Formal Concept Analysis, but adapted to ordered contexts. We also discuss a general method to mine these rules that can be easily incorporated into any algorithm for mining closed sequences, of which there are already some in the literature.