Data dictionary extraction for robust emergency detection

In this work we aim at generating association rules starting from meteorological measurements from a set of heterogeneous sensors displaced in a region. To create rules starting from the statistical dis- tribution of the data we adaptively extracted dictionaries of values. We used these dictionaries to reduce the data dimensionality and represent the values in a symbolic form. This representation is driven by the set of values in the training set and is suitable for the extraction of rules with traditional methods. Furthermore we adopt the boosting technique to build strong classifiers out of simpler association rules: their use shows promising results with respect to their accuracy a sensible increase in performance.