Abstract
Association rules (AR) represent a consolidated tool in data mining applications as they are able to discover regularities in large data sets. The information mined by the rules is very often difficult to exploit because of the presence of too many associations where to detect the really relevant logical implications. In this framework, by combining methodological and graphical pruning techniques, AR post-analysis tools are proposed. The methodological techniques will ensure the statistical significance of the AR which were not pruned, while the graphical ones will provide interactive and powerful visualization tools.
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