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Traditional association mining often produces large numbers of association rules and sometimes it is very difficult for users to understand such rules and applying this knowledge to any business process. In order to overcome the drawback of association rule mining and to find actionable knowledge from resultant association rules, a novel idea of combined patterns is used here. Combined Mining is a kind of post processing method for association rules generated. In this approach, first the association rules are filtered by varying support and confidence levels, then using the interestingness measure Irule , association rules are further extracted. Here , the approach is applied on a survey dataset and the results prove that the method is very efficient than the traditional mining approach for obtaining actionable rules. The scheme of combined association rule mining can be extended for combined rule pairs and combined rule clusters. The efficiency can be further improved by the parallel implementation of this approach.