Efficient subsequence matching using the longest common subsequence with a dual match index

Tae Sik Han, Seung Kyu Ko, Jaewoo Kang

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

18 Citations (Scopus)

Abstract

The purpose of subsequence matching is to find a query sequence from a long data sequence. Due to the abundance of applications, many solutions have been proposed. Virtually all previous solutions use the Euclidean measure as the basis for measuring distance between sequences. Recent studies, however, suggest that the Euclidean distance often fails to produce proper results due to the irregularity in the data, which is not so uncommon in our problem domain. Addressing this problem, some non-Euclidean measures, such as Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), have been proposed. However, most of the previous work in this direction focused on the whole sequence matching problem where query and data sequences are the same length. In this paper, we propose a novel subsequence matching framework using a non-Euclidean measure, in particular, LCS, and a new index query scheme. The proposed framework is based on the Dual Match framework where data sequences are divided into a series of disjoint equi-length subsequences and then indexed in an R-tree. We introduced similarity bound for index matching with LCS. The proposed query matching scheme reduces significant numbers of false positives in the match result. Furthermore, we developed an algorithm to skip expensive LCS computations through observing the warping paths. We validated our framework through extensive experiments using 48 different time series datasets. The results of the experiments suggest that our approach significantly improves the subsequence matching performance in various metrics.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 5th International Conference, MLDM 2007, Proceedings
PublisherSpringer Verlag
Pages585-600
Number of pages16
ISBN (Print)9783540734987
DOIs
Publication statusPublished - 2007
Event5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007 - Leipzig, Germany
Duration: 2007 Jul 182007 Jul 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4571 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007
CountryGermany
CityLeipzig
Period07/7/1807/7/20

Keywords

  • Dual match
  • Longest common subsequence
  • Subsequence matching

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Han, T. S., Ko, S. K., & Kang, J. (2007). Efficient subsequence matching using the longest common subsequence with a dual match index. In Machine Learning and Data Mining in Pattern Recognition - 5th International Conference, MLDM 2007, Proceedings (pp. 585-600). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4571 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-73499-4_44