Date: Tue Apr 22 13:27:41 2014

Title: Fast Statistical Assessment for Combinatorial Hypotheses Based on Frequent Itemset Mining

Authors: Shin-ichi Minato, Takeaki Uno, Koji Tsuda, Aika Terada, and Jun Sese


  • First name: Shin-ichi
  • Last name: Minato
  • Address: Graduate School of Information Science and Technology, Hokkaido University, North 14 West 9, Sapporo, 060- 0814, Japan
  • Email:

Abstract. In many scientific communities using experiment databases, one of the crucial problem is how to assess the statistical significance (P-value) of a discovered hypothesis. Especially, combinatorial hypothesis assessment is a hard problem because it requires a multiple-testing procedure with a very large factor of the P-value correction. Recently, Terada et al. proposed a novel method of the P-value correction, called "Limitless Arity Multiple-testing Procedure" (LAMP), which is based on frequent itemset enumeration to exclude meaninglessly infrequent itemsets which never be significant. The LAMP makes much more accurate P-value correction than previous method, and it empowers the scientific discovery. However, the original LAMP implementation is sometimes too time-consuming for practical databases. We propose a new LAMP algorithm that essentially executes itemset mining algorithm once, while the previous one executes many times. Our experimental results show that the proposed method is much (10 to 100 times) faster than the original LAMP. This algorithm enables us to discover significant P-value patterns in quite short time even for very large-scale databases.

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