Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Pattern Structures and Their Projections
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
Minimum description length principle: generators are preferable to closed patterns
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
An intersection inequality for discrete distributions and related generation problems
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Mining gene expression data with pattern structures in formal concept analysis
Information Sciences: an International Journal
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Coupled attribute analysis on numerical data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Semi-supervised learning on closed set lattices
Intelligent Data Analysis
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We investigate the problem of mining numerical data with Formal Concept Analysis. The usual way is to use a scaling procedure -transforming numerical attributes into binary ones- leading either to a loss of information or of efficiency, in particular w.r.t. the volume of extracted patterns. By contrast, we propose to directlywork on numerical data in a more precise and efficient way. For that, the notions of closed patterns, generators and equivalent classes are revisited in the numerical context. Moreover, two algorithms are proposed and tested in an evaluation involving real-world data, showing the quality of the present approach.