Machine learning is a suitable pattern recognition technique for detecting correlations between data. In the case of unsupervised learning, the groups formed from these correlations can receive a label, which consists of describing them in terms of their most relevant attributes and their respective ranges of values so that they are understood automatically. In this research work, this process is called labeling. However, a challenge for researchers is establishing the optimal number of clusters that best represent the underlying structure of the data subjected to clustering. This optimal number may vary depending on the data set and the grouping method used and influences the data clustering process and, consequently, the interpretability of the generated groups. Therefore, this research aims to provide an inference approach to the number of clusters to be used in the grouping based on the range of attribute values, followed by automatic data labeling based on the standard deviation to maximize the understanding of the groups obtained. This methodology was applied to four databases. The results show that it contributes to the interpretation of the groups since it generates more accurate labels without any overlap between ranges of values, considering the same attribute in different groups.
Com o crescimento cada vez maior da informatização, o gerenciamento do fluxo de informação acaba sendo um dos desafios de muitas empresas, no qual constantes problemas com o gerenciamento dessas informações mostram-se preocupantes. Esse trabalho tem como objetivo apresentar a importância do gerenciamento de fluxo de informação em uma organização, através do uso de práticas que o ITIL pode oferecer e agregar valor ao negócio. A metodologia utilizada foi de revisão bibliográfica baseada em artigos e trabalhos científicos, e junto a isso, foi desenvolvido um estudo de caso na Imobiliária RR fazendo um levantamento dos recursos e capacidades disponíveis e possíveis melhorias aplicadas às boas práticas do ITIL.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.