Mining Periodic Patterns with a MDL Criterion

Esther Galbrun, Peggy Cellier, Nikolaj Tatti, Alexandre Termier, Bruno Crémilleux

ECML-PKDD 2018 pp. 535-551

doi:10.1007/978-3-030-10928-8_32 /ecmlpkdd/2018/galbrun2018ecmlpkdd-mining/

Abstract

The quantity of event logs available is increasing rapidly, be they produced by industrial processes, computing systems, or life tracking, for instance. It is thus important to design effective ways to uncover the information they contain. Because event logs often record repetitive phenomena, mining periodic patterns is especially relevant when considering such data. Indeed, capturing such regularities is instrumental in providing condensed representations of the event sequences.

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Cite

Text

Galbrun et al. "Mining Periodic Patterns with a MDL Criterion." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2018. doi:10.1007/978-3-030-10928-8_32

Markdown

[Galbrun et al. "Mining Periodic Patterns with a MDL Criterion." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2018.](https://mlanthology.org/ecmlpkdd/2018/galbrun2018ecmlpkdd-mining/) doi:10.1007/978-3-030-10928-8_32

BibTeX

@inproceedings{galbrun2018ecmlpkdd-mining,
  title     = {{Mining Periodic Patterns with a MDL Criterion}},
  author    = {Galbrun, Esther and Cellier, Peggy and Tatti, Nikolaj and Termier, Alexandre and Crémilleux, Bruno},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2018},
  pages     = {535-551},
  doi       = {10.1007/978-3-030-10928-8_32},
  url       = {https://mlanthology.org/ecmlpkdd/2018/galbrun2018ecmlpkdd-mining/}
}