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arXiv · 1002.1104

An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets

Adam Kirsch·Michael Mitzenmacher·Andrea Pietracaprina·Geppino Pucci·Eli Upfal·Fabio Vandin
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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.

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Adam Kirsch, Michael Mitzenmacher, Andrea Pietracaprina, Geppino Pucci, Eli Upfal, Fabio Vandin. 2010-02-04. An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets. https://arxiv.org/abs/1002.1104

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