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Author Kirsch, Adam ♦ Mitzenmacher, Michael ♦ Pietracaprina, Andrea ♦ Pucci, Geppino ♦ Upfal, Eli ♦ Vandin, Fabio
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Frequent itemset mining ♦ Poisson approximation ♦ False discovery rate ♦ Multi-hypothesis test ♦ Statistical significance
Abstract As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications. In this work, we address significance in the context of frequent itemset mining. Specifically, we develop a novel methodology to identify a meaningful support threshold $s^{*}$ for a dataset, such that the number of itemsets with support at least $s^{*}$ represents a substantial deviation from what would be expected in a random dataset with the same number of transactions and the same individual item frequencies. These itemsets can then be flagged as statistically significant with a small false discovery rate. We present extensive experimental results to substantiate the effectiveness of our methodology.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-06-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 59
Issue Number 3
Page Count 22
Starting Page 1
Ending Page 22

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Source: ACM Digital Library