Frequent pattern mining with non-overlapping inversions

Da Jung Cho, Yo Sub Han, Hwee Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Frequent pattern mining is widely used in bioinformatics since frequent patterns in bio sequences often correspond to residues conserved during evolution. In bio sequence analysis, non-overlapping inversions are well-studied because of their practical properties for local sequence comparisons. We consider the problem of finding frequent patterns in a bio sequence with respect to non-overlapping inversions, and design efficient algorithms.

Original languageEnglish
Title of host publicationLanguage and Automata Theory and Applications - 9th International Conference, LATA 2015, Proceedings
EditorsAdrian-Horia Dediu, Carlos Martín-Vide, Enrico Formenti, Bianca Truthe
PublisherSpringer Verlag
Pages121-132
Number of pages12
ISBN (Electronic)9783319155784
DOIs
Publication statusPublished - 2015 Jan 1
Event9th International Conference on Language and Automata Theory and Applications, LATA 2015 - Nice, France
Duration: 2015 Mar 22015 Mar 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8977
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Language and Automata Theory and Applications, LATA 2015
CountryFrance
CityNice
Period15/3/215/3/6

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Cho, D. J., Han, Y. S., & Kim, H. (2015). Frequent pattern mining with non-overlapping inversions. In A-H. Dediu, C. Martín-Vide, E. Formenti, & B. Truthe (Eds.), Language and Automata Theory and Applications - 9th International Conference, LATA 2015, Proceedings (pp. 121-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8977). Springer Verlag. https://doi.org/10.1007/978-3-319-15579-1_9