WCDS: A Two-Phase Weightless Neural System for Data Stream Clustering

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017

Autores

Cardoso, DO (Autor) Outra; Franca, FMG (Autor) Outra; João Gama (Autor) FEP

Revista

Título: New Generation Computing

Vol. 35 Nº 4

Páginas: 391-416

ISSN: 0288-3635

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 7 Citações

Scopus - 10 Citações

Outras Informações

ID Authenticus: P-00N-3QT

DOI: 10.1007/s00354-017-0018-y

Abstract (EN): Clustering is a powerful and versatile tool for knowledge discovery, able to provide a valuable information for data analysis in various domains. To perform this task based on streaming data is quite challenging: outdated knowledge needs to be disposed while the current knowledge is obtained from fresh data; since data are continuously flowing, strict efficiency constraints have to be met. This paper presents WCDS, an approach to this problem based on the WiSARD artificial neural network model. This model already had useful characteristics as inherent incremental learning capability and patent functioning speed. These were combined with novel features as an adaptive countermeasure to cluster imbalance, a mechanism to discard expired data, and offline clustering based on a pairwise similarity measure for WiSARD discriminators. In an insightful experimental evaluation, the proposed system had an excellent performance according to multiple quality standards. This supports its applicability for the analysis of data streams.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 26