A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications.
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| Title: | A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications. |
|---|---|
| Authors: | Danesh, Malihe1,2 (AUTHOR), Dorrigiv, Morteza2 (AUTHOR) dorrigiv@semnan.ac.ir, Yaghmaee, Farzin2 (AUTHOR) |
| Source: | Expert Systems. Jul2023, Vol. 40 Issue 6, p1-20. 20p. |
| Subjects: | Probabilistic databases, Cluster analysis (Statistics), Graph algorithms, Taxonomy |
| Abstract: | Given the growth of uncertainty in the real world, analysing probabilistic graphs is crucial. Clustering is one of the most fundamental methods of mining probabilistic graphs to discover the hidden patterns in them. This survey examines an extensive and organized analysis of the clustering techniques of large probabilistic graphs proposed in the literature. First, the definition of probabilistic graphs and modelling them are introduced. Second, the clustering of such graphs and their challenges, such as uncertainty of edges, high dimensions, and the impossibility of applying certain graph clustering techniques directly, are expressed. Then, a taxonomy of clustering approaches is discussed in two main categories: threshold‐based and possible worlds‐based methods. The techniques presented in each category are explained and examined. Here, these methods are evaluated on real datasets, and their performance is compared with each other. Finally, the survey is summarized by describing some of the applications of probabilistic graph clustering and future research directions. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 164116262 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Danesh%2C+Malihe%22">Danesh, Malihe</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dorrigiv%2C+Morteza%22">Dorrigiv, Morteza</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> dorrigiv@semnan.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Yaghmaee%2C+Farzin%22">Yaghmaee, Farzin</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems%22">Expert Systems</searchLink>. Jul2023, Vol. 40 Issue 6, p1-20. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Probabilistic+databases%22">Probabilistic databases</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+algorithms%22">Graph algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Taxonomy%22">Taxonomy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Given the growth of uncertainty in the real world, analysing probabilistic graphs is crucial. Clustering is one of the most fundamental methods of mining probabilistic graphs to discover the hidden patterns in them. This survey examines an extensive and organized analysis of the clustering techniques of large probabilistic graphs proposed in the literature. First, the definition of probabilistic graphs and modelling them are introduced. Second, the clustering of such graphs and their challenges, such as uncertainty of edges, high dimensions, and the impossibility of applying certain graph clustering techniques directly, are expressed. Then, a taxonomy of clustering approaches is discussed in two main categories: threshold‐based and possible worlds‐based methods. The techniques presented in each category are explained and examined. Here, these methods are evaluated on real datasets, and their performance is compared with each other. Finally, the survey is summarized by describing some of the applications of probabilistic graph clustering and future research directions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=164116262 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/exsy.13248 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Probabilistic databases Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Graph algorithms Type: general – SubjectFull: Taxonomy Type: general Titles: – TitleFull: A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Danesh, Malihe – PersonEntity: Name: NameFull: Dorrigiv, Morteza – PersonEntity: Name: NameFull: Yaghmaee, Farzin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 02664720 Numbering: – Type: volume Value: 40 – Type: issue Value: 6 Titles: – TitleFull: Expert Systems Type: main |
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