Regular Decomposition of Multivariate Time Series and Other Matrices

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    10 Citations (Scopus)


    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
    EditorsPasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo
    Place of PublicationBerlin-Heidelberg
    ISBN (Print)978-3-662-44414-6
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication
    EventJoint IAPR International Workshop, S+SSPR 2014 - Joensuu, Finland
    Duration: 20 Aug 201422 Aug 2014

    Publication series

    SeriesLecture Notes in Computer Science


    ConferenceJoint IAPR International Workshop, S+SSPR 2014


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