Multiple hypothesis testing in pattern discovery

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Journal Title
Journal ISSN
Volume Title
Faculty of Information and Natural Sciences | D4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitys
Date
2009
Major/Subject
Mcode
Degree programme
Language
en
Pages
31
Series
TKK reports in information and computer science, 21
Abstract
The problem of multiple hypothesis testing arises when there are more than one hypothesis to be tested simultaneously for statistical significance. This is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entails a host of hypothesis, one for each itemset. A multiple hypothesis testing method is needed to control the number of false positives (Type I error). Our contribution in this paper is to extend the multiple hypothesis framework to be used with a generic data mining algorithm. We provide a method that provably controls the family-wise error rate (FWER, the probability of at least one false positive) in the strong sense. We evaluate the performance of our solution on both real and generated data. The results show that our method controls the FWER while maintaining the power of the test.
Description
Keywords
multiple hypothesis testing, randomization, empirical p-values, frequent itemsets, pattern mining
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Permanent link to this item
https://urn.fi/urn:nbn:fi:tkk-013062