### Abstract

Original language | English |
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Title of host publication | Structural, Syntactic, and Statistical Pattern Recognition |

Subtitle of host publication | Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings |

Editors | Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo |

Place of Publication | Berlin-Heidelberg |

Publisher | Springer |

Pages | 424-433 |

ISBN (Print) | 978-3-662-44414-6 |

DOIs | |

Publication status | Published - 2014 |

MoE publication type | D2 Article in professional manuals or guides or professional information systems or text book material |

### Publication series

Series | Lecture Notes in Computer Science |
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Volume | 8621 |

### Fingerprint

### Cite this

*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

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*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.

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Professional

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

SN - 978-3-662-44414-6

T3 - Lecture Notes in Computer Science

SP - 424

EP - 433

BT - Structural, Syntactic, and Statistical Pattern Recognition

A2 - Fränti, Pasi

A2 - Brown, Gavin

A2 - Loog, Marco

A2 - Escolano, Francisco

A2 - Pelillo, Marcello

PB - Springer

CY - Berlin-Heidelberg

ER -