Clustering data streams with weightless neural networks

• Type: Article in International Conference Proceedings Book
• Date: 2010

Authors

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

Conference proceedings International

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

Pages: 201-206

Indexing

Scopus - 10 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific