Extracting generic basis of association rules from SAGE data

Applying classical association rule extraction framework to dense SAGE data leads to an unmanageably highly sized association rule sets– compounded with their low precision– that often make the perusal of knowledge ineffective, their exploitation time-consuming, and frustrating for the user. To overcome such drawback, we advocate the extraction and exploitation of compact and informative generic basis of association rules. Obtained preliminary results highlight that the extracted correlations may be of help in identifying a gene functional groups and thus contribute to their annotation. From a biologic point of view, such identification may be a powerful verification technique for hampering gene mis-annotating or badly clustering in the Unigene library.