Data stream clustering using complex networks

  • Danilo A. Nunes Universidade Federal de Uberlândia
  • Murillo G. Carneiro Universidade Federal de Uberlândia

Resumo


O agrupamento em fluxo de dados é uma tarefa de aprendizado de máquina crucial para vários sistemas que geram dados de maneira contínua e carecem de analisá-los ininterruptamente. Através de recursos oferecidos pela plataforma MOA (Massive Online Analysis), a proposta desta pesquisa consiste em aplicar modelos de aprendizado baseados em redes complexas na fase offline do CluStream, na qual micro-grupos são agrupados através do algoritmo kMeans. Para os experimentos desse projeto, foram consideradas três bases de dados e várias medidas de desempenho específicas ao problema. Os resultados mostraram que o uso de redes complexas apresenta desempenho competitivo, chegando a superar o método tradicional k-Means em diversos cenários.

Palavras-chave: Fluxo de Dados, Agrupamento, Aprendizado de Máquina, Complex Networks

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Publicado
25/09/2023
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NUNES, Danilo A.; CARNEIRO, Murillo G.. Data stream clustering using complex networks. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 1210-1224. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2023.234716.