A density-based clustering approach for behavior change detection in data streams

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2012

Autores

Rosane Mafei (Autor) Outra; Andre Carvalho (Autor) Outra; José Filho (Autor) Outra; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: Proceedings - Brazilian Symposium on Neural Networks, SBRN

Páginas: 37-42

2012 Brazilian Conference on Neural Networks, SBRN 2012

Curitiba, Parana, 20 October 2012 through 25 October 2012

Indexação

Scopus - 4 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Físicas > Ciência de computadores

Outras Informações

ID Authenticus: P-008-918

DOI: 10.1109/sbrn.2012.22

Abstract (EN): Mining data streams poses many challenges to existing Machine Learning algorithms. Algorithms designed to learn in this scenario need to constantly update their decision models in accordance with current data behavior. Therefore, the ability to detect when the behavior of the stream is changing is an important feature of any learning technique approaching data streams. This work is concerned with unsupervised behavior change detection. It suggests the use of density-based clustering and an entropy measurement for change detection that is independent of the number and format of clusters. The proposed approach uses a modified version of the Den Stream algorithm that is designed to better cope with the entropy calculation. Experimental results using synthetic data provide insight on how clustering and novelty detection algorithms can be used for change detection in data streams. © 2012 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica