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

Abstract

This paper presents a proposal for an algorithm for fuzzy clustering based on maximum likelihood processing over the data stream. The adopted methodology consists of eliminating initialization problems and mathematical determinations (convergence), related to the computational implementation, via the analysis of the distance norm between the data samples and the cluster centers, as well as the use of fuzzy type-2 interval systems to create realistic prototypes to clusters. Results related to benchmark data clustering and computational complexity analysis illustrate the efficiency of the proposed methodology compared to other clustering algorithms presented in the literature.

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