An Efficient and Fast Algorithm for Mining Frequent Patterns on Multiple Biosequences

Abstract : Mining frequent patterns on biosequences is one of the important research fields in biological data mining. Traditional frequent pattern mining algorithms may generate large amount of short candidate patterns in the process of mining which cost more computational time and reduce the efficiency. In order to overcome such shortcoming of the traditional algorithms, we present an algorithm named MSPM for fast mining frequent patterns on biosequences. Based on the concept of primary patterns, the algorithm focuses on longer patterns for mining in order to avoid producing lots of short patterns. Meanwhile by using prefix tree of primary frequent patterns, the algorithm can extend the primary patterns and avoid plenty of irrelevant patterns. Experimental results show that MSPM can achieve mining results efficiently and improves the performance.
Type de document :
Communication dans un congrès
Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-344 (Part I), pp.178-194, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18333-1_22〉
Liste complète des métadonnées

Littérature citée [24 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01559564
Contributeur : Hal Ifip <>
Soumis le : lundi 10 juillet 2017 - 17:27:56
Dernière modification le : mardi 18 juillet 2017 - 15:30:23
Document(s) archivé(s) le : mercredi 24 janvier 2018 - 16:39:12

Fichier

978-3-642-18333-1_22_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Wei Liu, Ling Chen. An Efficient and Fast Algorithm for Mining Frequent Patterns on Multiple Biosequences. Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-344 (Part I), pp.178-194, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18333-1_22〉. 〈hal-01559564〉

Partager

Métriques

Consultations de la notice

73

Téléchargements de fichiers

51