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開催日2017年7月28日(金曜日)
開催時間10時30分-11時30分
発表者小宮山 純平 
発表者の紹介東京大学 助教
タイトルStatistical Emerging Pattern Mining with Multiple Testing Correction 
発表の概要
Emerging patterns are patterns whose support significantly differs between two databases. We study the problem of listing emerging patterns with a multiple testing guarantee. Recently, Terada et al. proposed the Limitless Arity Multiple-testing Procedure (LAMP) that controls the family-wise error rate (FWER) in statistical association mining. LAMP reduces the number of ``untestable’’ hypotheses without compromising its statistical power. Still, FWER is restrictive, and as a result, its statistical power is inherently unsatisfying when the number of patterns is large.

On the other hand, the false discovery rate (FDR) is less restrictive than FWER, and thus controlling FDR yields a larger number of significant patterns. We propose two emerging pattern mining methods: the first one controls FWER, and the second one controls FDR. The effectiveness of the methods is verified in computer simulations with real-world datasets.
開催場所VBL 301B
接続サイト神田オフィス