Regular Decomposition of Multivariate Time Series and Other Matrices

    Research output: Chapter in Book/Report/Conference proceedingChapter or book articleProfessional

    6 Citations (Scopus)

    Abstract

    We describe and illustrate a novel algorithm for clustering a large number of time series into few 'regular groups'. Our method is inspired by the famous Szemerédi's Regularity Lemma (SRL) in graph theory. SRL suggests that large graphs and matrices can be naturally 'compressed' by partitioning elements in a small number of sets. These sets and the patterns of relations between them present a kind of structure of large objects while the more detailed structure is random-like. We develop a maximum likelihood method for finding such 'regular structures' and applied it to the case of smart meter data of households. The resulting structure appears as more informative than a structure found by k-means. The algorithm scales well with data size and the structure itself becomes more apparent with bigger data size. Therefore, our method could be useful in a broader context of emerging big data
    Original languageEnglish
    Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition
    Subtitle of host publicationJoint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings
    EditorsPasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo
    Place of PublicationBerlin-Heidelberg
    PublisherSpringer
    Pages424-433
    ISBN (Print)978-3-662-44414-6
    DOIs
    Publication statusPublished - 2014
    MoE publication typeD2 Article in professional manuals or guides or professional information systems or text book material

    Publication series

    SeriesLecture Notes in Computer Science
    Volume8621

    Fingerprint

    Time series
    Smart meters
    Decomposition
    Graph theory
    Maximum likelihood
    Big data

    Cite this

    Reittu, H., Weiss, R., & Bazso, F. (2014). Regular Decomposition of Multivariate Time Series and Other Matrices. In P. Fränti, G. Brown, M. Loog, F. Escolano, & M. Pelillo (Eds.), Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings (pp. 424-433). Berlin-Heidelberg: Springer. Lecture Notes in Computer Science, Vol.. 8621 https://doi.org/10.1007/978-3-662-44415-3
    Reittu, Hannu ; Weiss, Robert ; Bazso, F. / Regular Decomposition of Multivariate Time Series and Other Matrices. Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings. editor / Pasi Fränti ; Gavin Brown ; Marco Loog ; Francisco Escolano ; Marcello Pelillo. Berlin-Heidelberg : Springer, 2014. pp. 424-433 (Lecture Notes in Computer Science, Vol. 8621).
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    abstract = "We describe and illustrate a novel algorithm for clustering a large number of time series into few 'regular groups'. Our method is inspired by the famous Szemer{\'e}di's Regularity Lemma (SRL) in graph theory. SRL suggests that large graphs and matrices can be naturally 'compressed' by partitioning elements in a small number of sets. These sets and the patterns of relations between them present a kind of structure of large objects while the more detailed structure is random-like. We develop a maximum likelihood method for finding such 'regular structures' and applied it to the case of smart meter data of households. The resulting structure appears as more informative than a structure found by k-means. The algorithm scales well with data size and the structure itself becomes more apparent with bigger data size. Therefore, our method could be useful in a broader context of emerging big data",
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    Reittu, H, Weiss, R & Bazso, F 2014, Regular Decomposition of Multivariate Time Series and Other Matrices. in P Fränti, G Brown, M Loog, F Escolano & M Pelillo (eds), Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings. Springer, Berlin-Heidelberg, Lecture Notes in Computer Science, vol. 8621, pp. 424-433. https://doi.org/10.1007/978-3-662-44415-3

    Regular Decomposition of Multivariate Time Series and Other Matrices. / Reittu, Hannu; Weiss, Robert; Bazso, F.

    Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings. ed. / Pasi Fränti; Gavin Brown; Marco Loog; Francisco Escolano; Marcello Pelillo. Berlin-Heidelberg : Springer, 2014. p. 424-433 (Lecture Notes in Computer Science, Vol. 8621).

    Research output: Chapter in Book/Report/Conference proceedingChapter or book articleProfessional

    TY - CHAP

    T1 - Regular Decomposition of Multivariate Time Series and Other Matrices

    AU - Reittu, Hannu

    AU - Weiss, Robert

    AU - Bazso, F

    N1 - Project code: 86270-1.4.2 InGRID_IND-COMPET: Data analysi

    PY - 2014

    Y1 - 2014

    N2 - We describe and illustrate a novel algorithm for clustering a large number of time series into few 'regular groups'. Our method is inspired by the famous Szemerédi's Regularity Lemma (SRL) in graph theory. SRL suggests that large graphs and matrices can be naturally 'compressed' by partitioning elements in a small number of sets. These sets and the patterns of relations between them present a kind of structure of large objects while the more detailed structure is random-like. We develop a maximum likelihood method for finding such 'regular structures' and applied it to the case of smart meter data of households. The resulting structure appears as more informative than a structure found by k-means. The algorithm scales well with data size and the structure itself becomes more apparent with bigger data size. Therefore, our method could be useful in a broader context of emerging big data

    AB - We describe and illustrate a novel algorithm for clustering a large number of time series into few 'regular groups'. Our method is inspired by the famous Szemerédi's Regularity Lemma (SRL) in graph theory. SRL suggests that large graphs and matrices can be naturally 'compressed' by partitioning elements in a small number of sets. These sets and the patterns of relations between them present a kind of structure of large objects while the more detailed structure is random-like. We develop a maximum likelihood method for finding such 'regular structures' and applied it to the case of smart meter data of households. The resulting structure appears as more informative than a structure found by k-means. The algorithm scales well with data size and the structure itself becomes more apparent with bigger data size. Therefore, our method could be useful in a broader context of emerging big data

    U2 - 10.1007/978-3-662-44415-3

    DO - 10.1007/978-3-662-44415-3

    M3 - Chapter or book article

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    EP - 433

    BT - Structural, Syntactic, and Statistical Pattern Recognition

    A2 - Fränti, Pasi

    A2 - Brown, Gavin

    A2 - Loog, Marco

    A2 - Escolano, Francisco

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    Reittu H, Weiss R, Bazso F. Regular Decomposition of Multivariate Time Series and Other Matrices. In Fränti P, Brown G, Loog M, Escolano F, Pelillo M, editors, Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings. Berlin-Heidelberg: Springer. 2014. p. 424-433. (Lecture Notes in Computer Science, Vol. 8621). https://doi.org/10.1007/978-3-662-44415-3