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
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
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Factor Analysis of Incidence Data via Novel Decomposition of Matrices
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Power-Law Distributions in Empirical Data
SIAM Review
Factorizing three-way binary data with triadic formal concepts
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Mining a new fault-tolerant pattern type as an alternative to formal concept discovery
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
| Hi-index | 0.00 |
A novel approach to triclustering of a three-way binary data is proposed. Tricluster is defined in terms of Triadic Formal Concept Analysis as a dense triset of a binary relation Y, describing relationship between objects, attributes and conditions. This definition is a relaxation of a triconcept notion and makes it possible to find all triclusters and triconcepts contained in triclusters of large datasets. This approach generalizes the similar study of concept-based biclustering.