论文信息 - Clustering data streams using a forgetful neural model

Clustering data streams using a forgetful neural model

To cluster a data stream is a more challenging task than its regular batch version, having stricter performance constraints. In this paper an approach to this problem is presented, based on WiSARD, a memory-based artificial neural network (ANN) model. This model functioning was reviewed and improved, in order to adapt it to this task. The experimental results obtained support the use of this system for the analysis of data streams in an informative way.

João Gama | Felipe Maia Galvão França | Douglas de O. Cardoso

[1] João Gama,et al. A bounded neural network for open set recognition , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[2] I. Aleksander,et al. WISARD·a radical step forward in image recognition , 1984 .

[3] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[4] W S McCulloch,et al. A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[5] Vipin Kumar,et al. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series , 2008 .

[6] João Gama,et al. A Weightless Neural Network-Based Approach for Stream Data Clustering , 2012, IDEAL.

[7] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Data stream clustering: A survey , 2013, CSUR.

[8] Geoff Holmes,et al. Active Learning With Drifting Streaming Data , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[9] Denis J. Dean,et al. Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables , 1999 .