A fuzzy data reduction cluster method based on boundary information for large datasets
Carregando...
Data
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Minas Gerais
Descrição
Tipo
Artigo de periódico
Título alternativo
Primeiro orientador
Membros da banca
Resumo
The fuzzy c-means algorithm (FCM) is aimed at computing the membership degree of each data point to its corresponding cluster center. This computation needs to calculate the distance matrix between the cluster center and the data point. The main bottleneck of the FCM algorithm is the computing of the membership matrix for all data points. This work presents a new clustering method, the bdrFCM (boundary data reduction fuzzy c-means). Our algorithm is based on the original FCM proposal, adapted to detect and remove the boundary regions of clusters. Our implementation efforts are directed in two aspects: processing large datasets in less time and reducing the data volume, maintaining the quality of the clusters. A significant volume of real data application (> 106 records) was used, and we identified that bdrFCM implementation has good scalability to handle datasets with millions of data points.
Abstract
Assunto
Computação
Palavras-chave
Data reduction techniques can be considered a useful strategy to handle the heterogeneity and massiveness of big datasets by reducing the high data volume into a manageable size. One way to use data reduction in big datasets is to apply sampling approaches. Usually, these methods extract some piece of information from big datasets without resorting to high-performance computing.
Citação
Departamento
Curso
Endereço externo
https://link.springer.com/article/10.1007/s00521-019-04049-4