Neural Network-based Framework for Data Stream Mining

摘要

We address the problem of mining data streams using Artificial Neural Networks (ANN). Usual data stream clustering models (e.g. k-means) are too dependent on assumptions regarding cluster statistical properties (i.e. number of clusters, cluster shape), while unsupervised ANN algorithms (Adaptive Resonant Theory - ART networks and Self-Organizing Maps - SOM) are recognized widely by their ability to discover hidden patterns, generalization capabilities and robustness to noise. However, use of ANNs with the data stream model is still poorly explored. We propose a methodology and modular framework to cluster data streams and extract other relevant knowledge. Empirical results with both synthetic and real data provide evidence of the validity of the approach.