Frequent episodes hidden in an event sequence describe the behavioral regularities of users or systems. Existing algorithms yield good results for mining frequent episodes under their respective definitions of support, but each of them is difficult or impossible to directly mine frequent episodes when the definition of support is changed. To meet the needs of changeable support definitions of users, an algorithm called FEM-DFS (frequent episode mining-depth first search) is proposed to mine frequent episodes in this paper. After scanning the event sequence one pass, FEM-DFS finds frequent episodes in a depth first search fashion, stores frequent episodes in a shared prefix/suffix tree and compresses the search space of frequent episodes by utilizing monotonicity, prefix monotonicity or suffix monotonicity. Experimental evaluation demonstrates the effectiveness of the proposed algorithm.