document: Empirical bayes screening for multi-item associations This paper considers the framework of the so-called `` market basket problem '' , in which a database of transactions is mined for the occurrence of unusually frequent item sets . In our case , `` unusually frequent '' involves estimates of the frequency of each item set divided by a baseline frequency computed as if items occurred independently . The focus is on obtaining reliable estimates of this measure of interestingness for all item sets , even item sets with relatively low frequencies . For example , in a medical database of patient histories , unusual item sets including the item `` patient death '' ( or other serious adverse event ) might hopefully be flagged with as few as 5 or 10 occurrences of the item set , it being unacceptable to require that item sets occur in as many as 0.1 % of millions of patient reports before the data mining algorithm detects a signal . Similar considerations apply in fraud detection applications . Thus we abandon the requirement that interesting item sets must contain a relatively large fixed minimal support , and adopt a criterion based on the results of fitting an empirical Bayes model to the item set counts . The model allows us to define a 95 % Bayesian lower confidence limit for the `` interestingness '' measure of every item set , whereupon the item sets can be ranked according to their empirical Bayes confidence limits . For item sets of size J ) 2 , we also distinguish between multi-item associations that can be explained by the observed J ( J-1 ) \/ 2 pairwise associations , and item sets that are significantly more frequent than their pairwise associations would suggest . Such item sets can uncover complex or synergistic mechanisms generating multi-item associations . This methodology has been applied within the U.S. Food and Drug Administration ( FDA ) to databases of adverse drug reaction reports and within AT&T to customer international calling histories . We also present graphical techniques for exploring and understanding the modeling results .
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