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Article . 2007
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Formal Concept Analysis in Relational Database and Rough Relational Database

Formal concept analysis in relational database and rough relational database
Authors: Jiang, Feng; Sui, Yuefei; Cao, Cungen;

Formal Concept Analysis in Relational Database and Rough Relational Database

Abstract

Since its foundation in the early 1980's, Formal ConceptAnalysis (FCA) has been used in many applications in data analysis, information retrieval, and knowledge discovery. In this paper, we suggest to exploit the framework of relational database model (RDM) and rough relational database model (RRDM) for Formal Concept Analysis. The basic idea is as follows. We firstly treat any relation (R,A) of RDM as a many-valued context of FCA. But for the rough relations of RRDM, we define a special kind of many-valued context – rough-relational context in FCA (In this kind of context, every attribute value is a subset, but not an element, of the corresponding attribute domain), and treat any rough relation (R,A) of RRDMas a rough-relational context of FCA. Correspondingly, the definitions for concepts or rough concepts in context or rough-relational context (R,A) are given. The basic properties about these concepts or rough concepts in (R,A) are also discussed.

Related Organizations
Keywords

formal concept analysis, Knowledge representation, Database theory, relational database model, rough sets, rough relational database model

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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