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A Descriptive Tolerance Nearness Measure for Performing Graph Comparison

Henry C. J., Awais S. A.

Fundamenta Informaticae

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2018

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Vol. 163, nr 4

305--324

EN

This article proposes the tolerance nearness measure (TNM) as a computationally reduced alternative to the graph edit distance (GED) for performing graph comparisons. The TNM is defined within the context of near set theory, where the central idea is that determining similarity between sets of disjoint objects is at once intuitive and practically applicable. The TNM between two graphs is produced using the Bron-Kerbosh maximal clique enumeration algorithm. The result is that the TNM approach is less computationally complex than the bipartite-based GED algorithm. The contribution of this paper is the application of TNM to the problem of quantifying the similarity of disjoint graphs and that the maximal clique enumeration-based TNM produces comparable results to the GED when applied to the problem of content-based image processing, which becomes important as the number of nodes in a graph increases.

2

Modification of Near Sets Theory

Marei E. A., Abd El-Monsef M. E., Abu-Donia H. M.

Fundamenta Informaticae

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2015

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Vol. 137, nr 3

387--402

EN

Most real life situations need some sort of approximation to fit mathematical models. The beauty of using topology in approximation is achieved via obtaining approximation for qualitative subsets without coding or using assumption. The aim of this paper is to introduce different approaches to near sets by using general relations and special neighborhoods. Some fundamental properties and characterizations are given. We obtain a comparison between these new approximations and traditional approximations introduced by Peters [23].

3

Treasure Trove at Banacha. Set Patterns in Descriptive Proximity Spaces

Peters J.F., Ramanna S.

Fundamenta Informaticae

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2013

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Vol. 127, nr 1-4

357--367

EN

This paper introduces descriptive set patterns that originated from our visits with Zdzisław Pawlak and Andrzej Skowron at Banacha and environs in Warsaw. This paper also celebrates the generosity and caring manner of Andrzej Skowron, who made our visits to Warsaw memorable events. The inspiration for the recent discovery of descriptive set patterns can be traced back to our meetings at Banacha. Descriptive set patterns are collections of near sets that arise rather naturally in the context of an extension of Solomon Leader's uniform topology, which serves as a base topology for compact Hausdorff spaces that are proximity spaces. The particular form of proximity space (called EF-proximity) reported here is an extension of the proximity space introduced by V. Efremovič during the first half of the 1930s. Proximally continuous functions introduced by Yu.V. Smirnov in 1952 lead to pattern generation of comparable set patterns. Set patterns themselves were first considered by T. Pavlidis in 1968 and led to U. Grenander's introduction of pattern generators during the 1990s. This article considers descriptive set patterns in EF-proximity spaces and their application in digital image classification. Images belong to the same class, provided each image in the class contains set patterns that resemble each other. Image classification then reduces to determining if a set pattern in a test image is near a set pattern in a query image.

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Sufficiently Near Neighbourhoods of Points in Flow Graphs. A Near Set Approach

Peters J.F., Chitcharoen D.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

175--196

EN

This paper introduces sufficiently near visual neighbourhoods of points and neighbourhoods of sets in digital image flow graphs (NDIFGs). An NDIFG is an extension of a Pawlak flow graph. The study of sufficiently near neighbourhoods in NDIFGs stems from recent work on near sets and topological spaces via near and far, especially in terms of visual neighbourhoods of points that are sufficiently near each other. From a topological perspective, non-spatially near sets represent an extension of proximity space theory and the original insight concerning spatially near sets by F. Riesz at the International Congress of Mathematicians (ICM) in 1908. In the context of Herrlich nearness, sufficiently near neighbourhoods of sets in NDIFGs provide a new perspective on topological structures in NDIFGs. The practical implications of this work are significant. With the advent of a study of the nearness of open as well as closed neighbourhods of points and of sets in NDIFGs, it is now possible to do information mining on a more global level and achieve new insights concerning the visual information embodied in the images that provide input to an NDIFG.

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Nearness of Sets in Local Admissible Covers : Theory and Application in Micropalaeontology

Peters J.F.

Fundamenta Informaticae

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2013

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Vol. 126, nr 4

433--444

EN

This paper considers the nearness of sets in local descriptive admissible covers of nonempty sets and the problem of quantifying the nearness of such sets. A brief review of descriptive Efremovič spaces as well descriptive intersection and union provides a foundation for the study of descriptive admissible covers. Descriptively near sets in admissible covers contain sequences of points with members having similar descriptions. The motivation for this approach stems from the need to consider fine-grained neighbourhoods of points in admissible covers that facilitate highly accurate measures of nearness of tiny parts of sets of objects of interest. A practical application of local admissible covers is given in terms of micropalaeontology and the detection of minute similarities and differences in microfossils, useful in the study of climate change, mineral and fossil fuel exploration.

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Development of Near Sets Within the Framework of Axiomatic Fuzzy Sets

Wang G., Ren Y., Liu X.

Fundamenta Informaticae

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2012

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Vol. 118, nr 3

291-304

EN

Near sets result from a generalization of rough sets, which introduced by Peters in 2006, and later formally defined in 2007. Near set theory provides a new framework for representation of objects characterized by the features that describe them. AFS (Axiomatic Fuzzy Set) theory was proposed by Liu (1998), which is a semantic methodology relating to the fuzzy theory. In this paper, a new version of near sets based on AFS theory is established, in which every object has an AFS fuzzy description with definitely semantics. The proposed approach to assessing the nearness (closeness) of objects is not defined directly using a distance metric, but depend on similarity of their fuzzy descriptions. It is also a natural linguistic description that is similar to humans perception. Moreover, an approach to set approximation based on the union of families of objects with similar fuzzy descriptions is given. The near sets based on AFS theory can be viewed as a new development of near sets within the fuzzy context.