Efficient mining of association rules using closed itemset lattices
Information Systems
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
The Modern Algebra of Information Retrieval
The Modern Algebra of Information Retrieval
International Journal of Intelligent Systems - Decision Sciences: Foundations and Applications
Combinatorics: The Rota Way
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Let L be a lattice. A function f:L-R (usually called evaluation) is submodular if f(x@?y)+f(x@?y)@?f(x)+f(y), supermodular if f(x@?y)+f(x@?y)=f(x)+f(y), and modular if it is both submodular and supermodular. Modular functions on a finite lattice form a finite dimensional vector space. For finite distributive lattices, we compute this (modular) dimension. This turns out to be another characterization of distributivity (Theorem 3.9). We also present a correspondence between isotone submodular evaluations and closure operators on finite lattices (Theorem 5.5). This interplay between closure operators and evaluations should be understood as building a bridge between qualitative and quantitative data analysis.