Extracting compact and information lossless set of fuzzy association rules

Applying classical association rule extraction framework on fuzzy data sets leads to an unmanageably highly sized association rule sets -compounded with an information loss due to the discretization operation -that often constitutes a hamper towards an efficient exploitation of the mined knowledge. To overcome such drawback, we advocate the extraction and the exploitation of compact and informative generic basis of fuzzy association rules. This generic basis constitutes a compact nucleus of fuzzy association rules. In addition, we introduce an axiomatic system to ensure the derivation mechanism of all the remaining rules. Obtained preliminary results are very encouraging and they highlight a very important reduction of the number of the extracted fuzzy association rules without information loss.