The effective construction of many association rule bases requires the computation of both frequent closed and frequent generator itemsets (FCIs/FGs). However, only few miners address both concerns, typically by applying levelwise breadth-first traversal. As depth-first traversal is known to be superior, we examine 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, 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.