Wyniki wyszukiwania - Biblioteka Nauki

1

Perception and Classification. A Note on Near Sets and Rough Sets

100%

Wolski M.

Fundamenta Informaticae

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2010

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tom Vol. 101, nr 1/2

143-155

EN

The paper aims to establish topological links between perception of objects (as it is defined in the framework of near sets) and classification of these objects (as it is defined in the framework of rough sets). In the near set approach, the discovery of near sets (i.e. sets containing objects with similar descriptions) starts with the selection of probe functions which provide a basis for describing and discerning objects. On the other hand, in the rough set approach, the classification of objects is based on object attributes which are collected into information systems (or data tables). As is well-known, an information system can be represented as a topological space (U, τ_E). If we pass froman approximation space (U,E) to the quotient space U/E, where points represent indiscernible objects of U, then U/E will be endowed with the discrete topology induced (via the canonical projection) by τ_E. The main objective of this paper is to show how probe functions can provide new topologies on the quotient set U/E and, in consequence, new (perceptual) topologies on U.

2

Near Groups of Weak Cosets on Nearness Approximation Spaces

80%

Őztürk M. A. , Uçkun M. , İnan E.

Fundamenta Informaticae

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2014

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tom Vol. 133, nr 4

433--448

EN

In this paper, we consider the problem of how to establish algebraic structures of near sets on nearness approximation spaces. Essentially, our approach is define the near group of all weak cosets by considering an operation on the set of all weak cosets. Afterwards, our aim is to study near homomorphism theorems on near groups, and investigate some properties of near groups.

3

Near Approximations in Modules

70%

Davvaz B. , Setyawati D. W. , Soleha , Mukhlash I. , Subiono

Foundations of Computing and Decision Sciences

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2021

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tom Vol. 46, No. 4

319--337

EN

Rough set theory is a mathematical approach to imperfect knowledge. The near set approach leads to partitions of ensembles of sample objects with measurable information content and an approach to feature selection. In this paper, we apply the previous results of Bagirmaz [Appl. Algebra Engrg. Comm. Comput., 30(4) (2019) 285-29] and [Davvaz et al., Near approximations in rings. AAECC (2020). https://doi.org/10.1007/s00200-020-00421-3] to module theory. We introduce the notion of near approximations in a module over a ring, which is an extended notion of a rough approximations in a module presented in [B. Davvaz and M. Mahdavipour, Roughness in modules, Information Sciences, 176 (2006) 3658-3674]. Then we define the lower and upper near submodules and investigate their properties.

4

Measuring Resemblances Between Swarm Behaviours: A Perceptual Tolerance Near Set Approach

60%

Ramanna S. , Meghdadi A. H.

Fundamenta Informaticae

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2009

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tom Vol. 95, nr 4

533-552

EN

The problem considered in this article is how to detect and measure resemblances between swarm behaviours. The solution to this problem stems from an extension of recent work on tolerance near sets and image correspondence. Instead of considering feature extraction from subimages in digital images, we compare swarm behaviours by considering feature extraction from subsets of tuples of feature-values representing the behaviour of observed swarms of organisms. Thanks to recent work on the foundations of near sets, it is possible to formulate a rigorous approach to measuring the extent that swarm behaviours resemble each other. Fundamental to this approach is what is known as a recent description-based set intersection, a set containing objects with matching or almost the same descriptions extracted from objects contained in pairs of disjoint sets. Implicit in this work is a new approach to comparing information tables representing N. Tinbergen’s ethology (study of animal behaviour) and direct result of recent work on what is known as rough ethology. Included in this article is a comparison of recent nearness measures that includes a new form of F. Hausdorff’s distance measure. The contribution of this article is a tolerance near set approach to measuring the degree of resemblance between swarm behaviours.

5

Near Sets. Special Theory about Nearness of Objects

51%

Peters J. F.

Fundamenta Informaticae

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2007

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tom Vol. 75, nr 1-4

407-433

EN

The problem considered in this paper is how to approximate sets of objects that are qualitatively but not necessarily spatially near each other. The term qualitatively near is used here to mean closeness of descriptions or distinctive characteristics of objects. The solution to this problem is inspired by the work of Zdzisaw Pawlak during the early 1980s on the classification of objects by means of their attributes. This article introduces a special theory of the nearness of objects that are either static (do not change) or dynamic (change over time). The basic approach is to consider a link relation, which is defined relative to measurements associated with features shared by objects independent of their spatial relations. One of the outcomes of this work is the introduction of new forms of approximations of objects and sets of objects. The nearness of objects can be approximated using rough set methods. The proposed approach to approximation of objects is a straightforward extension of the rough set approach to approximating objects, where approximation can be considered in the context of information granules (neighborhoods). In addition, the usual rough set approach to concept approximation has been enriched by an increase in the number of granules (neighborhoods) associated with the classification of a concept as near to its approximation. A byproduct of the proposed approximation method is what we call a near set. It should also be observed that what is presented in this paper is considered a special (not a general) theory about nearness of objects. The contribution of this article is an approach to nearness as a vague concept which can be approximated from the state of objects and domain knowledge.