Discovering significant rules

In many applications, association rules will only be interesting if they represent non-trivial correlations between all constituent items. Numerous techniques have been developed that seek to avoid false discoveries. However, while all provide useful solutions to aspects of this problem, none provides a generic solution that is both flexible enough to accommodate varying definitions of true and false discoveries and powerful enough to provide strict control over the risk of false discoveries. This paper presents generic techniques that allow definitions of true and false discoveries to be specified in terms of arbitrary statistical hypothesis tests and which provide strict control over the experimentwise risk of false discoveries. Categories and Subject Descriptors: H.2.8 [Database Management] Database Applications: data mining General Terms: Algorithms, Performance, Reliability, Experimentation

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Geoffrey I. Webb
• Keywords: Arbitrary Statistical Hypothesis, Data Mining, False Discoveries, KDD 2006, Strict Control