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Erschienen in:
Approximations from Anywhere and General Rough Sets Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Attribute Reduction in Rough Set Theory and Formal Concept Analysis

verfasst von : María José Benítez-Caballero, Jesús Medina, Eloísa Ramírez-Poussa

Erschienen in: Rough Sets

Verlag: Springer International Publishing

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Abstract

Rough Set Theory (RST) and Formal Concept Analysis (FCA) are two mathematical tools for data analysis which, in spite of considering different philosophies, are closely related. In this paper, we study the relation between the attribute reduction mechanisms in FCA and in RST. Different properties will be introduced which provide a new size reduction mechanism in FCA based on the philosophy of RST.

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Vorheriges Kapitel A Neural Network Pattern Recognition Approach to Automatic Rainfall Classification by Using Signal Strength in LTE/4G Networks
Nächstes Kapitel Toward Interactive Attribute Selection with Infolattices – A Position Paper
Fußnoten
1
We assume that the reader is familiar with the notions related to classical theory of propositional logic [8, 12].
 
2
Originally these operators were denoted as \('\) by Ganter and Wille and they were called derivation operators. In order to differentiate between the mapping on the set of objects and on the set of attributes, we have changed the notation.
 
3
Note that the discernibility matrix is symmetric due to the discernibility relation is reflexive.
 
4
In order to simplify the notation, we will write \((^{\uparrow _1},^{\downarrow ^1})\) and \((^{\uparrow _2},^{\downarrow ^2})\), instead of \((^{\uparrow _{D_1}},^{\downarrow ^{D_1}})\) and \((^{\uparrow _{D_2}},^{\downarrow ^{D_2}})\) to denote the concept-forming operators in the reduced contexts by \(D_1\) and \(D_2\), respectively.
 
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Metadaten
Titel
Attribute Reduction in Rough Set Theory and Formal Concept Analysis
verfasst von
María José Benítez-Caballero
Jesús Medina
Eloísa Ramírez-Poussa
Copyright-Jahr
2017
Buch
Rough Sets

Print ISBN: 978-3-319-60839-6

Electronic ISBN: 978-3-319-60840-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-60840-2_37

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