Clustering data streams with weightless neural networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2010

Autores

Cardoso, DO (Autor) Outra; Lima, PMV (Autor) Outra; De Gregorio, M (Autor) Outra; João Gama (Autor) FEP; Franca, FMG (Autor) Outra

Ata de Conferência Internacional

Título: ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Páginas: 201-206

Indexação

Scopus - 10 Citações

Outras Informações

ID Authenticus: P-008-G75

Abstract (EN): Producing good quality clustering of data streams in real time is a difficult problem, since it is necessary to perform the analysis of data points arriving in a continuous style, with the support of quite limited computational resources. The incremental and evolving nature of the resulting clustering structures must reflect the dynamics of the target data stream. The WiSARD weightless perceptron, and its associated DRASiW extension, are intrinsically capable of, respectively, performing one-shot learning and producing prototypes of the learnt categories. This work introduces a simple generalization of RAM-based neurons in order to explore both weightless neural models in the data stream clustering problem.

Idioma: Inglês

Tipo (Avaliação Docente): Científica