An Interval Type-2 Maximum Likelihood Fuzzy Clustering Algorithm

• Ben-Hur Matthews Moreno Montel UFMA
• Ginalber Luiz de Oliveira Serra IFMA

DOI: https://doi.org/10.5753/eniac.2022.226935

Resumo

Este artigo apresenta a proposta de um algoritmo para agrupamento nebuloso baseada no processamento de máxima verossimilhança sobre o fluxo de dados. A metodologia adotada consiste em eliminar problemas de inicialização e iderteminações matemáticas (convergência), relativas à implementação computacional, via análise da norma de distância entre as amostras dos dados e os centros dos agrupamentos, bem como no uso de sistemas nebulosos tipo-2 intervalares para a criação de protótipos realísticos aos agrupamentos. Resultados relacionados ao arupamento de dados benchmark e análise da velocidade de convergência ilustram a eficiência da metodologia proposta em comparação a outros algoritmos de agrupamento apresentados na literatura.

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