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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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Victoria Erofeeva (21807723) (author)
مؤلفون آخرون:	Oleg Granichin (21807726) (author), Vikentii Pankov (21807729) (author), Zeev Volkovich (9611574) (author)
منشور في:	2025
الموضوعات:	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
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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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