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Visualization has become an essential support throughout the KDD process in order to extract hidden information from huge amount of data. Visual data exploration techniques provide the user with graphic views or metaphors that represent potential patterns and data relationships. However, an only image does not always convey high–dimensional data properties successfully. From such data sets, visualization techniques have to deal with the curse of dimensionality in a critical way, as the number of examples may be very small with respect to the number of attributes. In this work, we describe a visual exploration technique that automatically extracts relevant attributes and displays their ranges of interest in order to support two data mining tasks: classification and feature selection. Through different metaphors with dynamic properties, the user can re-explore meaningful intervals belonging to the most relevant attributes, building decision rules and increasing the model accuracy interactively

Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper describes a classification system based on decision rules that may store up–to–date border examples to avoid unnecessary revisions when virtual drifts are present in data. Consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbour algorithm. In addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another.