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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.

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Název: 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.
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: 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.
Druh dokumentu: still image
Jazyk: unknown
DOI: 10.1371/journal.pone.0327396.g003
Dostupnost: https://doi.org/10.1371/journal.pone.0327396.g003
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
Rights: CC BY 4.0
Přístupové číslo: edsbas.7E4862F1
Databáze: BASE
Popis
Abstrakt: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.
DOI:10.1371/journal.pone.0327396.g003