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.