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On Scaling of Fuzzy FCA to Pattern Structures

Aleksey Buzmakov 1 Amedeo Napoli 2 
2 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : FCA is a mathematical formalism having many applications in data mining and knowledge discovery. Originally it deals with binary data tables. However, there is a number of extensions that enrich standard FCA. In this paper we consider two important extensions: fuzzy FCA and pattern structures, and discuss the relation between them. In particular we introduce a scaling procedure that enables representing a fuzzy context as a pattern structure. Studying the relation between different extensions of FCA is of high importance, since it allows migrating methods from one extension to another. Moreover, it allows for more simple implementation of different extensions within a software.
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Submitted on : Wednesday, December 21, 2016 - 1:22:45 PM
Last modification on : Thursday, August 4, 2022 - 5:18:46 PM
Long-term archiving on: : Monday, March 20, 2017 - 5:34:26 PM


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  • HAL Id : hal-01421000, version 1


Aleksey Buzmakov, Amedeo Napoli. On Scaling of Fuzzy FCA to Pattern Structures. The 13th International Conference on Concept Lattices and their Applications (CLA2016), Jul 2016, Moscow, Russia. ⟨hal-01421000⟩



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