Abstract
mining high-utility patterns is the way of discovering sets of useful items that can provide a high profit in a customer transaction database. Discovering High-utility itemsets provide useful information that can help in decision making by clearly identify sets of lucrative items that customers bought in retail store. Discovering customer profitable items in retail store using traditional high-utility methods is inappropriate to find periodic customer behaviors and also in what manner those items related to each other's do. In this paper, we resolve those limitations by providing new method for discovering the productive high-utility periodic patterns from customer related data. Informally, we find the set of high profit correlated group of items. We define a new pattern-growth algorithm with new tree structure. Experimental evaluations show that our algorithm can reveal useful information.