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Cylindric extensions of interval-valued fuzzy sets in data linguistic summaries

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Abstract

The extension of fuzzy sets in which membership degrees are intervals in [0, 1], not only real numbers, is called interval-valued fuzzy sets (IVFSs). They include traditional fuzzy sets as particular cases, if looking at real membership degrees as at degenerated intervals. It is useful when real membership degrees are hard or even impossible to be determined, e.g. when experts differ in their opinions on membership degrees: the answer is correct to a degree of 0.5 up to 0.7. The paper presents applications of IVFSs and their cylindric extensions to representation of linguistic data. We introduce cylindric extensions of IVFSs and we formalize unions and intersections of IVFSs in different universes of discourse via cylindric extensions, to represent properties of objects described by attributes having their values in different domains, e.g. tall in cm and rich in €. We apply the described methods to data linguistic summarization in the sense of Yager (1982) but based on IVFSs. In particular, we propose to re-express some algorithms using cylindric extensions to make computations simplified and more human-oriented.

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Notes

  1. Fuzzy sets in which membership degrees are fuzzy sets themselves, usually, fuzzy numbers. In case of IVFSs, those fuzzy numbers have rectangular and normal membership functions.

  2. In a broader context, one may discuss such expressions as labels of linguistic variables and their compatibility levels, cf. Zadeh (1975). See also interval-valued linguistic variables and their interval-valued compatibility levels Niewiadomski (2005); Niewiadomski (2008a).

  3. Actually, in computations, we do not differ objects and tuples being their models, because a system does not take into account any other attributes or properties but only those stored in the database.

  4. Detailed explanations on operations used in this formula are given in Niewiadomski (2008a). In particular, we use arithmetic operations on intervals proposed by Sengupta et al. (2001). Here, only the final form is presented.

  5. Broader explanations on the semantics of this quality measure are given in Kacprzyk et al. (2000) and Niewiadomski (2010).

  6. The interval-valued fuzzy set \({ce(S_{j})\cap {\mathcal{D}}}\) is represented by the membership functions \({\underline \mu_{{\textnormal{ce}}(S_j)}\restriction{\mathcal{D}}\colon {\mathcal{D}}\to [0,1], (\underline \mu_{{\rm{ce}}(S_{j})}\restriction {\mathcal{D}})(d_{i}) = \underline \mu_{{\rm{ce}}(S_{j})}(d_{i}), }\) and \({\overline\mu_{{\rm{ce}}(S_{j})}\restriction {\mathcal{D}}}\)—analogously.

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Correspondence to Adam Niewiadomski.

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Niewiadomski, A. Cylindric extensions of interval-valued fuzzy sets in data linguistic summaries. J Ambient Intell Human Comput 4, 369–376 (2013). https://doi.org/10.1007/s12652-011-0098-3

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