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Communication dans un congrès

A Decomposition Approach for Discovering Discriminative Motifs in a Sequence Database

Abstract : Considerable effort has been invested over the years in ad-hoc algorithms for item set and pattern mining. Constraint programming has recently been proposed as a means to tackle item set mining tasks within a general modelling framework. We follow this approach to address the discovery of discriminative n-ary motifs in databases of labeled sequences. We define a n-ary motif as a mapping of n patterns to n class-wide embeddings and we restrict the interpretation of constraints on a motif to the sequences embedding all patterns. We formulate core constraints that minimize redundancy between motifs and introduce a general constraint optimization framework to compute common and exclusive motifs. We cast the discovery of closed and replication-free motifs in this framework for which we propose a two-stage approach based on constraint programming. Experimental results on datasets of protein sequences demonstrate the efficiency of the approach.
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Communication dans un congrès
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Contributeur : Okina Univ Angers Connectez-vous pour contacter le contributeur
Soumis le : jeudi 10 juin 2021 - 14:16:47
Dernière modification le : mercredi 20 octobre 2021 - 03:19:08
Archivage à long terme le : : samedi 11 septembre 2021 - 19:04:19


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David Lesaint, Deepak Mehta, Barry O'Sullivan, Vincent Vigneron. A Decomposition Approach for Discovering Discriminative Motifs in a Sequence Database. ICTAI 2014 - 26th IEEE International Conference on Tools with Artificial Intelligence, 2014, Limassol, Cyprus. pp.544-551, ⟨10.1109/ICTAI.2014.88⟩. ⟨hal-03256752⟩



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