Multi-scale sequential pattern discovery and alignment for long-duration waveform similarity quantification and interpretation

Masaharu Goto, Naoki Kobayashi, Gang Ren, Mitsunori Ogihara

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

Abstract

Long-duration waveform similarity analysis identifies common waveform patterns among a group of signal recordings and uses these common waveform patterns to quantify the similarities of waveform signals. This quantification process of waveform similarity is an important analysis device for navigating, organizing, and interpreting long-duration waveform recordings. This paper proposes a computational framework for quantifying the timeline similarity relationships employing the sequential pattern analysis of long-duration recording signals at multiple analysis scales. Our proposed computational framework extends the scope of the conventional waveform similarity analysis toward more sophisticated pattern-matching approaches and enables in-depth explorations of larger datasets while allowing more quantitative interpretations and user interactions. The proposed similarity measurement framework is applied to two long-duration recording datasets to demonstrate its usage scenarios.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
EditorsPanagiotis Papapetrou, Xueqi Cheng, Qing He
PublisherIEEE Computer Society
Pages820-829
Number of pages10
ISBN (Electronic)9781728146034
DOIs
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2019-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
CountryChina
CityBeijing
Period11/8/1911/11/19

Keywords

  • long-duration waveform
  • pattern discovery
  • sequential pattern
  • signal processing
  • temporal timeline analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    Goto, M., Kobayashi, N., Ren, G., & Ogihara, M. (2019). Multi-scale sequential pattern discovery and alignment for long-duration waveform similarity quantification and interpretation. In P. Papapetrou, X. Cheng, & Q. He (Eds.), Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 (pp. 820-829). [8955656] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2019.00121