The effective construction of many association rule bases require the computation of both frequent closed and frequent generator itemsets (FCIs/FGs). However, these two tasks are rarely combined. Most of the existing solutions apply levelwise breadth-first traversal, though depth-first traversal is knowingly superior. Hence, we address here the depth-first FCI/FG-mining. The proposed algorithm, Touch, deals with both tasks separately, i.e., uses a well-known vertical method, Charm, to extract FCIs and a novel one called Talky-G, to extract FGs. The respective outputs are matched in a post-processing step. Experimental results indicate that Touch is highly efficient and outperforms its levelwise competitors.