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The classification of biological sequences is one of the significant challenges in bioinformatics as well for protein as for nucleic sequences. The presence of these data in huge masses, their ambiguity and especially the high costs of the in vitro analysisin terms of time and money, make the use of data mining rather a necessity than a rational choice. However, the data mining techniques, which often process data under the relational format, are confronted with the inappropriate format of the biological sequences. Hence, an inevitable step of pre-processing must be established. This work presents the biological sequences encoding as a preparation step before their classification. We present three existing encoding methods based on the motifs extraction. We also propose to improve one of these methods and we carry out a comparative study which takes into account, of course, the effect of each method on the classification accuracy but also the number of generated attributes and the CPU time. [ABSTRACT FROM AUTHOR]

The extraction of IF-THEN rules from data is a promising task of data mining including both Artificial Intelligence and Statistics. One of the difficulties encountered is how to evaluate the relevance of the extracted rules? Many authors use statistical interestingness measures to evaluate the relevance of each rule (taken alone). Recently, few research works have done a synthesis study of the existing interestingness measures but their study presents some limits. In this paper, firstly, we present an overview of related works studying more than forty interestingness measures. Secondly, we establish a list of nineteen other interestingness measures not referenced by the related works. Then, we identify twelve semantic properties characterizing the behavior of interestingness measures. Finally, we did a theoretical study of sixty two interestingness measures by outlining their semantic properties. The results of this study are useful to the users of a data-mining system in order to help them to choose an appropriate measure. [ABSTRACT FROM AUTHOR]