We address the problem of mining data streams using Artificial Neural Networks (ANN). Usual data stream clustering models (eg. k-means) are too de- pendent on assumptions regarding cluster statistical properties (ie. number of clus- ters, cluster shape), while unsupervised ANN algorithms (Adaptive Resonant The- ory — ART networks and Self-Organizing Maps — SOM) are recognized widely by their ability to discover hidden patterns, generalization capabilities and robust- ness to noise. However, use of ANNs with the data stream model is still poorly ex- plored. 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.
|Title of host publication||Frontiers in Artificial Intelligence and Applications|
|Publication status||Published - 1 Jan 2012|
|Event||ECAI - European Conference on Artificial Inteligence - |
Duration: 1 Jan 2012 → …
|Conference||ECAI - European Conference on Artificial Inteligence|
|Period||1/01/12 → …|