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Irredundant Coverings, Tolerances, and Related Algebras

Authors: Järvinen J.; Radeleczki, S.;

Irredundant Coverings, Tolerances, and Related Algebras

Abstract

This chapter deals with rough approximations defined by tolerance relations that represent similarities between the elements of a given universe of discourse. We consider especially tolerances induced by irredundant coverings of the universe U. This is natural in view of Pawlak’s original theory of rough sets defined by equivalence relations: any equivalence E on U is induced by the partition U∕E of U into equivalence classes, and U∕E is a special irredundant covering of U in which the blocks are disjoint. Here equivalence classes are replaced by tolerance blocks which are maximal sets in which all elements are similar to each other. The blocks of a tolerance R on U always form a covering of U which induces R, but this covering is not necessarily irredundant and its blocks may intersect. In this chapter we consider the semantics of tolerances in rough sets, and in particular the algebraic structures formed by the rough approximations and rough sets defined by different types of tolerances.

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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!
1
Average
Average
Average
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