Efficient mining of association rules using closed itemset lattices
Information Systems
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A Triadic Approach to Formal Concept Analysis
ICCS '95 Proceedings of the Third International Conference on Conceptual Structures: Applications, Implementation and Theory
How Triadic Diagrams Represent Conceptual Structures
ICCS '97 Proceedings of the Fifth International Conference on Conceptual Structures: Fulfilling Peirce's Dream
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining frequent closed cubes in 3D datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
TRIAS--An Algorithm for Mining Iceberg Tri-Lattices
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Constructing Iceberg Lattices from Frequent Closures Using Generators
DS '08 Proceedings of the 11th International Conference on Discovery Science
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Yet a Faster Algorithm for Building the Hasse Diagram of a Concept Lattice
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
About the lossless reduction of the minimal generator family of a context
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
Towards intensional answers to OLAP queries for analytical sessions
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Ternary and more generally n-ary relations are commonly found in real-life applications and data collections. In this paper, we define new notions and propose procedures to mine closed tri-sets (triadic concepts) and triadic association rules within the framework of triadic concept analysis. The input data is represented as a formal triadic context of the form K := (K1,K2,K3, Y), where K1, K2, and K3 are object, attribute and condition sets respectively, and Y is a ternary relation between the three sets. While dyadic association rules represent links between two groups of attributes (itemsets), triadic association rules can take at least three distinct forms. One of them is the following: A → C D, where A and D are subsets of K2, and C is a subset of K3. It states that A implies D under the conditions in C. In particular, the implication holds for any subset in C. The benefits of triadic association rules of this kind lie in the fact that they represent patterns in a more compact and meaningful way than association rules that can be extracted for example from the formal (dyadic) context K(1) := (K1, K2 × K3, Y(1)) with (ai, (aj, ak)) ε Y(1) : ⇔ (ai, aj, ak) ε Y.