Ubiquitous Self-Organizing Maps
Knowledge discovery in ubiquitous environments are usually conditioned by the data stream model, e.g., data is potentially infinite, arrives continuously and is subject to concept drift. These factors present additional challenges to standard data mining algorithms. Artificial Neural Networks (ANN) models are still poorly explored in these settings. State-of-the-art methods to deal with data streams are single-pass modifications of standard algorithms, e.g., Kmeans for clustering, and involve some relaxation of the quality of the results, i.e., since the data cannot be revisited to refine the models, the goal is to achieve good approximations.