Um Estudo Comparativo entre Algoritmos de Agrupamentos de Dados Usando a Ferramenta YADMT

• Narciso F. Sousa UFRN
• Flavius L. Gorgônio UFRN
• Huliane M. Silva IFRN

DOI: https://doi.org/10.5753/ercemapi.2021.17911

Abstract

The need to transform data into information and information into knowledge led to the emergence of the data mining area, whose objective is to provide techniques for interpreting large volumes of data. Although current computational tools for analyzing and processing information can analyze huge volumes of data in a matter of seconds, real-world applications tend to be much more complex and have much more challenging databases than those commonly presented in the literature. This work presents a comparative study between data clustering algorithms from Fundamental Clustering Problem Suite (FCPS) databases and the Yet Another Data Mining Tool (YADMT), which simulate various situations present in real world problems. The algorithms chosen in this research were: ant colony, k-means, self-organizing maps and hierarchical methods. For their evaluation, the F-Measure, the R-Index and the Intra-groups Variance were used.

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