A Decomposition Approach for Discovering Discriminative Motifs in a Sequence Database - Université d'Angers Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

A Decomposition Approach for Discovering Discriminative Motifs in a Sequence Database

Deepak Mehta
  • Fonction : Auteur
Barry O'Sullivan
  • Fonction : Auteur
  • PersonId : 849989

Résumé

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.
Fichier principal
Vignette du fichier
2014lesaintictai.pdf (294.84 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03256752 , version 1 (10-06-2021)

Identifiants

Citer

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⟩

Collections

UNIV-ANGERS LERIA
12 Consultations
42 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More