Determining Hit Rate in Pattern Search

The problem of spurious apparent patterns arising by chance is a fundamental one for pattern detection. Classical approaches, based on adjustments such as the Bonferroni procedure, are arguably not appropriate in a data mining context. Instead, methods based on the false discovery rate the proportion of flagged patterns which do not represent an underlying reality may be more relevant. We describe such procedures and illustrate their application on a marketing dataset.