Action Rules are rule based systems for discovering actionable patterns which are hidden in a large dataset. All recommended patterns from Action Rules incur some form of cost to the users. It is obvious that recommendations are interesting to the users only if the cost that the user pays for the recommended actions is low. In other words, the recommendations should be profitable or valuable to the user when they perform a chain of actions, at the lowest possible cost. In the modern era of big data, organizations are collecting massive amounts of data, growing constantly. Finding low cost actionable patterns for such large data in these domains, is time consuming and requires a scalable approach. In this work, we introduce the notion of Action Graph and propose an algorithm to search the Action Graph for actionable patterns of lowest cost. We apply the proposed algorithm to three datasets in transportation, medical, and business domains. Results show how these domains can benefit from the discovered actionable recommendations of low cost.