Steady Patterns

Willy Ugarte; Alexandre Termier; Miguel Santana

doi:10.1109/ICDMW.2016.0103

Steady Patterns

Willy Ugarte
, Alexandre Termier
, Miguel Santana

Université Grenoble Alpes
University of Rennes 1
STMicroelectronics SA, France

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Skypatterns are an elegant answer to the pattern explosion issue, when a set of measures can be provided. Skypatterns for all possible measure combinations can be explored thanks to recent work on the skypattern cube. However, this leads to too many skypatterns, where it is difficult to quickly identify which ones are more important. First, we introduce a new notion of pattern steadiness which measures the conservation of the skypattern property across the skypattern cube, allowing to see which are the 'most universal' skypatterns. Then, we extended this notion to partitions of the dataset, and show in our experiments that this both allows to discover especially stable skypatterns, and identify interesting differences between the partitions.

Original language	English
Title of host publication	Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Editors	Carlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
Publisher	IEEE Computer Society
Pages	692-699
Number of pages	8
ISBN (Electronic)	9781509054725
DOIs	https://doi.org/10.1109/ICDMW.2016.0103
State	Published - 2 Jul 2016
Externally published	Yes
Event	16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain Duration: 12 Dec 2016 → 15 Dec 2016

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	0
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Country/Territory	Spain
City	Barcelona
Period	12/12/16 → 15/12/16

Keywords

Pattern Mining
Skypattern
Skypattern Cube
Trace Analysis

Access to Document

10.1109/ICDMW.2016.0103

Cite this

Ugarte, W., Termier, A., & Santana, M. (2016). Steady Patterns. In C. Domeniconi, F. Gullo, F. Bonchi, F. Bonchi, J. Domingo-Ferrer, R. Baeza-Yates, R. Baeza-Yates, R. Baeza-Yates, Z.-H. Zhou, & X. Wu (Eds.), Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 (pp. 692-699). Article 7836734 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2016.0103

Ugarte, Willy ; Termier, Alexandre ; Santana, Miguel. / Steady Patterns. Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. editor / Carlotta Domeniconi ; Francesco Gullo ; Francesco Bonchi ; Francesco Bonchi ; Josep Domingo-Ferrer ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Zhi-Hua Zhou ; Xindong Wu. IEEE Computer Society, 2016. pp. 692-699 (IEEE International Conference on Data Mining Workshops, ICDMW).

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title = "Steady Patterns",

abstract = "Skypatterns are an elegant answer to the pattern explosion issue, when a set of measures can be provided. Skypatterns for all possible measure combinations can be explored thanks to recent work on the skypattern cube. However, this leads to too many skypatterns, where it is difficult to quickly identify which ones are more important. First, we introduce a new notion of pattern steadiness which measures the conservation of the skypattern property across the skypattern cube, allowing to see which are the 'most universal' skypatterns. Then, we extended this notion to partitions of the dataset, and show in our experiments that this both allows to discover especially stable skypatterns, and identify interesting differences between the partitions.",

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author = "Willy Ugarte and Alexandre Termier and Miguel Santana",

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editor = "Carlotta Domeniconi and Francesco Gullo and Francesco Bonchi and Francesco Bonchi and Josep Domingo-Ferrer and Ricardo Baeza-Yates and Ricardo Baeza-Yates and Ricardo Baeza-Yates and Zhi-Hua Zhou and Xindong Wu",

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Ugarte, W, Termier, A & Santana, M 2016, Steady Patterns. in C Domeniconi, F Gullo, F Bonchi, F Bonchi, J Domingo-Ferrer, R Baeza-Yates, R Baeza-Yates, R Baeza-Yates, Z-H Zhou & X Wu (eds), Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016., 7836734, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 0, IEEE Computer Society, pp. 692-699, 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016, Barcelona, Spain, 12/12/16. https://doi.org/10.1109/ICDMW.2016.0103

Steady Patterns. / Ugarte, Willy; Termier, Alexandre; Santana, Miguel.
Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. ed. / Carlotta Domeniconi; Francesco Gullo; Francesco Bonchi; Francesco Bonchi; Josep Domingo-Ferrer; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Zhi-Hua Zhou; Xindong Wu. IEEE Computer Society, 2016. p. 692-699 7836734 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Termier, Alexandre

AU - Santana, Miguel

PY - 2016/7/2

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N2 - Skypatterns are an elegant answer to the pattern explosion issue, when a set of measures can be provided. Skypatterns for all possible measure combinations can be explored thanks to recent work on the skypattern cube. However, this leads to too many skypatterns, where it is difficult to quickly identify which ones are more important. First, we introduce a new notion of pattern steadiness which measures the conservation of the skypattern property across the skypattern cube, allowing to see which are the 'most universal' skypatterns. Then, we extended this notion to partitions of the dataset, and show in our experiments that this both allows to discover especially stable skypatterns, and identify interesting differences between the partitions.

AB - Skypatterns are an elegant answer to the pattern explosion issue, when a set of measures can be provided. Skypatterns for all possible measure combinations can be explored thanks to recent work on the skypattern cube. However, this leads to too many skypatterns, where it is difficult to quickly identify which ones are more important. First, we introduce a new notion of pattern steadiness which measures the conservation of the skypattern property across the skypattern cube, allowing to see which are the 'most universal' skypatterns. Then, we extended this notion to partitions of the dataset, and show in our experiments that this both allows to discover especially stable skypatterns, and identify interesting differences between the partitions.

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KW - Skypattern

KW - Skypattern Cube

KW - Trace Analysis

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Ugarte W, Termier A, Santana M. Steady Patterns. In Domeniconi C, Gullo F, Bonchi F, Bonchi F, Domingo-Ferrer J, Baeza-Yates R, Baeza-Yates R, Baeza-Yates R, Zhou ZH, Wu X, editors, Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. IEEE Computer Society. 2016. p. 692-699. 7836734. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW.2016.0103

Steady Patterns

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Keywords

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