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Making good decisions at the top of a search tree is important for finding good solutions early in constraint optimization. In this paper, we propose a method employing frequent pattern mining (FPM), a classic datamining technique, to find good subtrees for solving constraint optimization problems. We demonstrate that applying FPM in a small number of random high-quality feasible solutions enables us to identify subtrees containing optimal solutions in more than 55% of problem instances for four real world benchmark problems. The method works as a plugin that can be combined with any search strategy for branch-and-bound search. Exploring the identified subtrees first, the method brings substantial improvements for four efficient search strategies in both total runtime and runtime of finding optimal solutions.