Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Pattern-growth methods for frequent pattern mining
Pattern-growth methods for frequent pattern mining
Custom asymmetric page split generalized index search trees and formal concept analysis
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
Spatial indexing for scalability in FCA
ICFCA'06 Proceedings of the 4th international conference on Formal Concept Analysis
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
A ConceptLink graph for text structure mining
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
Border algorithms for computing hasse diagrams of arbitrary lattices
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
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The paper presents a new border algorithm for making the covering relation of concepts explicit for iceberg concept lattices. The border algorithm requires no information from the formal context relying only on the formal concept set in order to explicitly state the covering relation between formal concepts. Empirical testing is performed to compare the border algorithm with a traditional algorithm based on the Covering Edges algorithm from Concept Data Analysis [4].