A Critical View on Automatic Significance-Filtering in Pattern Mining

Florian Lemmerich, Frank Puppe
Proceedings of the Workshop on Statistically Sound Data Mining at ECML/PKDD, PMLR 47:21-27, 2015.

Abstract

Statistically sound validation of results plays an important role in modern data mining. In this context, it has been advocated to disregard patterns that cannot be automatically confirmed as statistically valid by the available data. In this short position paper, we argue against a mandatory automatic significance filtering of results.

Cite this Paper

BibTeX
@InProceedings{pmlr-v47-lemmerich14a, title = {A Critical View on Automatic Significance-Filtering in Pattern Mining}, author = {Lemmerich, Florian and Puppe, Frank}, booktitle = {Proceedings of the Workshop on Statistically Sound Data Mining at ECML/PKDD}, pages = {21--27}, year = {2015}, editor = {Hämäläinen, Wilhelmiina and Petitjean, François and Webb, I.}, volume = {47}, series = {Proceedings of Machine Learning Research}, address = {Nancy, France}, month = {15 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v47/lemmerich14a.pdf}, url = {https://proceedings.mlr.press/v47/lemmerich14a.html}, abstract = {Statistically sound validation of results plays an important role in modern data mining. In this context, it has been advocated to disregard patterns that cannot be automatically confirmed as statistically valid by the available data. In this short position paper, we argue against a mandatory automatic significance filtering of results.} }
Endnote
%0 Conference Paper %T A Critical View on Automatic Significance-Filtering in Pattern Mining %A Florian Lemmerich %A Frank Puppe %B Proceedings of the Workshop on Statistically Sound Data Mining at ECML/PKDD %C Proceedings of Machine Learning Research %D 2015 %E Wilhelmiina Hämäläinen %E François Petitjean %E I. Webb %F pmlr-v47-lemmerich14a %I PMLR %P 21--27 %U https://proceedings.mlr.press/v47/lemmerich14a.html %V 47 %X Statistically sound validation of results plays an important role in modern data mining. In this context, it has been advocated to disregard patterns that cannot be automatically confirmed as statistically valid by the available data. In this short position paper, we argue against a mandatory automatic significance filtering of results.
RIS
TY - CPAPER TI - A Critical View on Automatic Significance-Filtering in Pattern Mining AU - Florian Lemmerich AU - Frank Puppe BT - Proceedings of the Workshop on Statistically Sound Data Mining at ECML/PKDD DA - 2015/11/27 ED - Wilhelmiina Hämäläinen ED - François Petitjean ED - I. Webb ID - pmlr-v47-lemmerich14a PB - PMLR DP - Proceedings of Machine Learning Research VL - 47 SP - 21 EP - 27 L1 - http://proceedings.mlr.press/v47/lemmerich14a.pdf UR - https://proceedings.mlr.press/v47/lemmerich14a.html AB - Statistically sound validation of results plays an important role in modern data mining. In this context, it has been advocated to disregard patterns that cannot be automatically confirmed as statistically valid by the available data. In this short position paper, we argue against a mandatory automatic significance filtering of results. ER -
APA
Lemmerich, F. & Puppe, F.. (2015). A Critical View on Automatic Significance-Filtering in Pattern Mining. Proceedings of the Workshop on Statistically Sound Data Mining at ECML/PKDD, in Proceedings of Machine Learning Research 47:21-27 Available from https://proceedings.mlr.press/v47/lemmerich14a.html.

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