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Ablation study results.

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Název: Ablation study results.
Autoři: Victoria Erofeeva, Oleg Granichin, Vikentii Pankov, Zeev Volkovich
Rok vydání: 2025
Témata: Cell Biology, Infectious Diseases, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, world datasets show, unlike traditional methods, trained neural network, dynamic network changes, system &# 8217, minimal communication overhead, efficient decentralized clustering, system adapts, clustering structure, clustering accuracy, %22">xlink ">, static topologies, paper presents, iot ), immediate results, highly suitable, generate compact, fly processing, dynamical multi, distributed environments, distributed aggregation, dimensionality reduction, data must, consistent summaries, consensus protocol
Popis: The paper presents a decentralized, real-time clustering method designed for large-scale, distributed environments such as the Internet of Things (IoT). The approach combines compressed sensing for dimensionality reduction with a consensus protocol for distributed aggregation, enabling each node to generate compact, consistent summaries of the system’s clustering structure with minimal communication overhead. These representations are processed by a pre-trained neural network to reconstruct the global clustering state entirely without centralized coordination. Unlike traditional methods that depend on static topologies and centralized computation, this system adapts to dynamic network changes and supports on-the-fly processing. The system suits IoT applications where data must be processed locally, and immediate results are essential. Experiments on both synthetic and real-world datasets show that the method significantly outperforms baseline approaches in clustering accuracy, making it highly suitable for resource-limited, decentralized IoT scenarios.
Druh dokumentu: dataset
Jazyk: unknown
Relation: https://figshare.com/articles/dataset/Ablation_study_results_/29667969
DOI: 10.1371/journal.pone.0327396.t004
Dostupnost: https://doi.org/10.1371/journal.pone.0327396.t004
https://figshare.com/articles/dataset/Ablation_study_results_/29667969
Rights: CC BY 4.0
Přístupové číslo: edsbas.B2DD1C35
Databáze: BASE
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  – Url: https://doi.org/10.1371/journal.pone.0327396.t004#
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  Data: Ablation study results.
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  Data: <searchLink fieldCode="AR" term="%22Victoria+Erofeeva%22">Victoria Erofeeva</searchLink><br /><searchLink fieldCode="AR" term="%22Oleg+Granichin%22">Oleg Granichin</searchLink><br /><searchLink fieldCode="AR" term="%22Vikentii+Pankov%22">Vikentii Pankov</searchLink><br /><searchLink fieldCode="AR" term="%22Zeev+Volkovich%22">Zeev Volkovich</searchLink>
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  Data: 2025
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  Data: <searchLink fieldCode="DE" term="%22Cell+Biology%22">Cell Biology</searchLink><br /><searchLink fieldCode="DE" term="%22Infectious+Diseases%22">Infectious Diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+Sciences+not+elsewhere+classified%22">Biological Sciences not elsewhere classified</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+Sciences+not+elsewhere+classified%22">Mathematical Sciences not elsewhere classified</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Systems+not+elsewhere+classified%22">Information Systems not elsewhere classified</searchLink><br /><searchLink fieldCode="DE" term="%22world+datasets+show%22">world datasets show</searchLink><br /><searchLink fieldCode="DE" term="%22unlike+traditional+methods%22">unlike traditional methods</searchLink><br /><searchLink fieldCode="DE" term="%22trained+neural+network%22">trained neural network</searchLink><br /><searchLink fieldCode="DE" term="%22dynamic+network+changes%22">dynamic network changes</searchLink><br /><searchLink fieldCode="DE" term="%22system+%26#+8217%22">system &# 8217</searchLink><br /><searchLink fieldCode="DE" term="%22minimal+communication+overhead%22">minimal communication overhead</searchLink><br /><searchLink fieldCode="DE" term="%22efficient+decentralized+clustering%22">efficient decentralized clustering</searchLink><br /><searchLink fieldCode="DE" term="%22system+adapts%22">system adapts</searchLink><br /><searchLink fieldCode="DE" term="%22clustering+structure%22">clustering structure</searchLink><br /><searchLink fieldCode="DE" term="%22clustering+accuracy%22">clustering accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22xlink+">%22">xlink "></searchLink><br /><searchLink fieldCode="DE" term="%22static+topologies%22">static topologies</searchLink><br /><searchLink fieldCode="DE" term="%22paper+presents%22">paper presents</searchLink><br /><searchLink fieldCode="DE" term="%22iot+%29%22">iot )</searchLink><br /><searchLink fieldCode="DE" term="%22immediate+results%22">immediate results</searchLink><br /><searchLink fieldCode="DE" term="%22highly+suitable%22">highly suitable</searchLink><br /><searchLink fieldCode="DE" term="%22generate+compact%22">generate compact</searchLink><br /><searchLink fieldCode="DE" term="%22fly+processing%22">fly processing</searchLink><br /><searchLink fieldCode="DE" term="%22dynamical+multi%22">dynamical multi</searchLink><br /><searchLink fieldCode="DE" term="%22distributed+environments%22">distributed environments</searchLink><br /><searchLink fieldCode="DE" term="%22distributed+aggregation%22">distributed aggregation</searchLink><br /><searchLink fieldCode="DE" term="%22dimensionality+reduction%22">dimensionality reduction</searchLink><br /><searchLink fieldCode="DE" term="%22data+must%22">data must</searchLink><br /><searchLink fieldCode="DE" term="%22consistent+summaries%22">consistent summaries</searchLink><br /><searchLink fieldCode="DE" term="%22consensus+protocol%22">consensus protocol</searchLink>
– Name: Abstract
  Label: Description
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  Data: The paper presents a decentralized, real-time clustering method designed for large-scale, distributed environments such as the Internet of Things (IoT). The approach combines compressed sensing for dimensionality reduction with a consensus protocol for distributed aggregation, enabling each node to generate compact, consistent summaries of the system’s clustering structure with minimal communication overhead. These representations are processed by a pre-trained neural network to reconstruct the global clustering state entirely without centralized coordination. Unlike traditional methods that depend on static topologies and centralized computation, this system adapts to dynamic network changes and supports on-the-fly processing. The system suits IoT applications where data must be processed locally, and immediate results are essential. Experiments on both synthetic and real-world datasets show that the method significantly outperforms baseline approaches in clustering accuracy, making it highly suitable for resource-limited, decentralized IoT scenarios.
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  Data: https://figshare.com/articles/dataset/Ablation_study_results_/29667969
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  Data: 10.1371/journal.pone.0327396.t004
– Name: URL
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  Data: https://doi.org/10.1371/journal.pone.0327396.t004<br />https://figshare.com/articles/dataset/Ablation_study_results_/29667969
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  Data: edsbas.B2DD1C35
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1371/journal.pone.0327396.t004
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      – Text: unknown
    Subjects:
      – SubjectFull: Cell Biology
        Type: general
      – SubjectFull: Infectious Diseases
        Type: general
      – SubjectFull: Biological Sciences not elsewhere classified
        Type: general
      – SubjectFull: Mathematical Sciences not elsewhere classified
        Type: general
      – SubjectFull: Information Systems not elsewhere classified
        Type: general
      – SubjectFull: world datasets show
        Type: general
      – SubjectFull: unlike traditional methods
        Type: general
      – SubjectFull: trained neural network
        Type: general
      – SubjectFull: dynamic network changes
        Type: general
      – SubjectFull: system &# 8217
        Type: general
      – SubjectFull: minimal communication overhead
        Type: general
      – SubjectFull: efficient decentralized clustering
        Type: general
      – SubjectFull: system adapts
        Type: general
      – SubjectFull: clustering structure
        Type: general
      – SubjectFull: clustering accuracy
        Type: general
      – SubjectFull: xlink ">
        Type: general
      – SubjectFull: static topologies
        Type: general
      – SubjectFull: paper presents
        Type: general
      – SubjectFull: iot )
        Type: general
      – SubjectFull: immediate results
        Type: general
      – SubjectFull: highly suitable
        Type: general
      – SubjectFull: generate compact
        Type: general
      – SubjectFull: fly processing
        Type: general
      – SubjectFull: dynamical multi
        Type: general
      – SubjectFull: distributed environments
        Type: general
      – SubjectFull: distributed aggregation
        Type: general
      – SubjectFull: dimensionality reduction
        Type: general
      – SubjectFull: data must
        Type: general
      – SubjectFull: consistent summaries
        Type: general
      – SubjectFull: consensus protocol
        Type: general
    Titles:
      – TitleFull: Ablation study results.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Victoria Erofeeva
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          Name:
            NameFull: Oleg Granichin
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            NameFull: Vikentii Pankov
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            NameFull: Zeev Volkovich
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
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