Efficient algorithms for similarity measures over sequential data: A look beyond kernels

Konrad Rieck, Pavel Laskov, Klaus Robert Müller

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

6 Citations (Scopus)

Abstract

Kernel functions as similarity measures for sequential data have been extensively studied in previous research. This contribution addresses the efficient computation of distance functions and similarity coefficients for sequential data. Two proposed algorithms utilize different data structures for efficient computation and yield a runtime linear in the sequence length. Experiments on network data for intrusion detection suggest the importance of distances and even non-metric similarity measures for sequential data.

Original languageEnglish
Title of host publicationPattern Recognition - 28th DAGM Symposium, Proceedings
PublisherSpringer Verlag
Pages374-383
Number of pages10
ISBN (Print)3540444122, 9783540444121
Publication statusPublished - 2006
Externally publishedYes
Event28th Symposium of the German Association for Pattern Recognition, DAGM 2006 - Berlin, Germany
Duration: 2006 Sep 122006 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4174 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other28th Symposium of the German Association for Pattern Recognition, DAGM 2006
CountryGermany
CityBerlin
Period06/9/1206/9/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Rieck, K., Laskov, P., & Müller, K. R. (2006). Efficient algorithms for similarity measures over sequential data: A look beyond kernels. In Pattern Recognition - 28th DAGM Symposium, Proceedings (pp. 374-383). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4174 LNCS). Springer Verlag.