@inproceedings{b25d5e9ca4124f49a4a3187852cbb107,

title = "Regular Decomposition of Multivariate Time Series and Other Matrices",

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",

author = "Hannu Reittu and Robert Weiss and F Bazso",

note = "Project code: 86270-1.4.2 InGRID_IND-COMPET: Data analysi; Joint IAPR International Workshop, S+SSPR 2014 ; Conference date: 20-08-2014 Through 22-08-2014",

year = "2014",

doi = "10.1007/978-3-662-44415-3",

language = "English",

isbn = "978-3-662-44414-6",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "424--433",

editor = "Pasi Fr{\"a}nti and Gavin Brown and Marco Loog and Francisco Escolano and { Pelillo}, Marcello",

booktitle = "Structural, Syntactic, and Statistical Pattern Recognition",

address = "Germany",

}