An incremental concept formation approach for learning from databases
Theoretical Computer Science - Special issue on formal methods in databases and software engineering
A fast algorithm for building lattices
Information Processing Letters
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Incremental Transformation of Lattices: A Key to Effective Knowledge Discovery
ICGT '02 Proceedings of the First International Conference on Graph Transformation
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Constructing Iceberg Lattices from Frequent Closures Using Generators
DS '08 Proceedings of the 11th International Conference on Discovery Science
From concepts to concept lattice: a border algorithm for making covers explicit
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Generalization of association rules through disjunction
Annals of Mathematics and Artificial Intelligence
Border algorithms for computing hasse diagrams of arbitrary lattices
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
Mining triadic association rules from ternary relations
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
A completeness analysis of frequent weighted concept lattices and their algebraic properties
Data & Knowledge Engineering
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Formal concept analysis (FCA) is increasingly applied to data mining problems, essentially as a formal framework for mining reduced representations (bases) of target pattern families. Yet most of the FCA-based miners, closed pattern miners, would only extract the patterns themselves out of a dataset, whereas the generality order among patterns would be required for many bases. As a contribution to the topic of the (precedence) order computation on top of the set of closed patterns, we present a novel method that borrows its overall incremental approach from two algorithms in the literature. The claimed innovation consists of splitting the update of the precedence links into a large number of lower-cover list computations (as opposed to a single upper-cover list computation) that unfold simultaneously. The resulting method shows a good improvement with respect to its counterpart both on its theoretical complexity and on its practical performance. It is therefore a good starting point for the design of efficient and scalable precedence miners.