Steady Patterns

Willy Ugarte; Alexandre Termier; Miguel Santana

doi:10.1109/ICDMW.2016.0103

Steady Patterns

Willy Ugarte, Alexandre Termier, Miguel Santana

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Editores	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
Editorial	IEEE Computer Society
Páginas	692-699
Número de páginas	8
ISBN (versión digital)	9781509054725
DOI	https://doi.org/10.1109/ICDMW.2016.0103
Estado	Publicada - 2 jul. 2016
Publicado de forma externa	Sí
Evento	16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Espana Duración: 12 dic. 2016 → 15 dic. 2016

Serie de la publicación

Nombre	IEEE International Conference on Data Mining Workshops, ICDMW
Volumen	0
ISSN (versión impresa)	2375-9232
ISSN (versión digital)	2375-9259

Conferencia

Conferencia	16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
País/Territorio	Espana
Ciudad	Barcelona
Período	12/12/16 → 15/12/16

Acceder al documento

10.1109/ICDMW.2016.0103

Otros archivos y enlaces

Enlace a la publicación en Scopus

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Ugarte, W., Termier, A., & Santana, M. (2016). Steady Patterns. En 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). Artículo 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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Ugarte, W, Termier, A & Santana, M 2016, Steady Patterns. En 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, Espana, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AU - Termier, Alexandre

AU - Santana, Miguel

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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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A2 - Baeza-Yates, Ricardo

A2 - Zhou, Zhi-Hua

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Ugarte W, Termier A, Santana M. Steady Patterns. En Domeniconi C, Gullo F, Bonchi F, Bonchi F, Domingo-Ferrer J, Baeza-Yates R, Baeza-Yates R, Baeza-Yates R, Zhou ZH, Wu X, editores, 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