Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index.
<p>Each metric is plotted against the number of agents. The proposed method demonstrates consistently superior performance compared to the baseline distributed approach. A slight degradation in clustering quality at 5,000 agents indicates potential scalability limitations, which may be address...
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2025
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| _version_ | 1852018114463531008 |
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| author | Victoria Erofeeva (21807723) |
| author2 | Oleg Granichin (21807726) Vikentii Pankov (21807729) Zeev Volkovich (9611574) |
| author2_role | author author author |
| author_facet | Victoria Erofeeva (21807723) Oleg Granichin (21807726) Vikentii Pankov (21807729) Zeev Volkovich (9611574) |
| author_role | author |
| dc.creator.none.fl_str_mv | Victoria Erofeeva (21807723) Oleg Granichin (21807726) Vikentii Pankov (21807729) Zeev Volkovich (9611574) |
| dc.date.none.fl_str_mv | 2025-07-29T17:34:21Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0327396.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Evaluation_of_the_proposed_algorithm_across_varying_agent_counts_1_000_3_000_and_5_000_using_four_metrics_Adjusted_Rand_Index_ARI_Normalized_Mutual_Information_NMI_Silhouette_Score_and_Davies-Bouldin_Index_/29667957 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 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 centralized computation |
| dc.title.none.fl_str_mv | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Each metric is plotted against the number of agents. The proposed method demonstrates consistently superior performance compared to the baseline distributed approach. A slight degradation in clustering quality at 5,000 agents indicates potential scalability limitations, which may be addressed through architectural modifications such as increased network depth or additional internal channels.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_787056969ab09fa026adfac97684ba4e |
| identifier_str_mv | 10.1371/journal.pone.0327396.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29667957 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index.Victoria Erofeeva (21807723)Oleg Granichin (21807726)Vikentii Pankov (21807729)Zeev Volkovich (9611574)Cell BiologyInfectious DiseasesBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedworld datasets showunlike traditional methodstrained neural networkdynamic network changessystem &# 8217minimal communication overheadefficient decentralized clusteringsystem adaptsclustering structureclustering accuracyxlink ">static topologiespaper presentsiot ).immediate resultshighly suitablegenerate compactfly processingdynamical multidistributed environmentsdistributed aggregationdimensionality reductiondata mustconsistent summariesconsensus protocolcentralized computation<p>Each metric is plotted against the number of agents. The proposed method demonstrates consistently superior performance compared to the baseline distributed approach. A slight degradation in clustering quality at 5,000 agents indicates potential scalability limitations, which may be addressed through architectural modifications such as increased network depth or additional internal channels.</p>2025-07-29T17:34:21ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0327396.g003https://figshare.com/articles/figure/Evaluation_of_the_proposed_algorithm_across_varying_agent_counts_1_000_3_000_and_5_000_using_four_metrics_Adjusted_Rand_Index_ARI_Normalized_Mutual_Information_NMI_Silhouette_Score_and_Davies-Bouldin_Index_/29667957CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296679572025-07-29T17:34:21Z |
| spellingShingle | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index. Victoria Erofeeva (21807723) 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 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 centralized computation |
| status_str | publishedVersion |
| title | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index. |
| title_full | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index. |
| title_fullStr | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index. |
| title_full_unstemmed | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index. |
| title_short | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index. |
| title_sort | Evaluation of the proposed algorithm across varying agent counts (1,000, 3,000, and 5,000), using four metrics: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Silhouette Score, and Davies-Bouldin Index. |
| topic | 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 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 centralized computation |