A　fuzzy　clustering　method　with　linguistic　information　is　introduced.　It　uses　a　minimizing　cross-entropy　model　to　avoid　setting　the　clustering　threshold　artificially.　During　the　clustering,　the　semantics　of　the　linguistic　information　is　conservatively　represented　by　solving　a　programming.　It　maximizes　the　potential　differences　between　the　objects　to　be　clustered,　and　further　helps　an　analyst　to　reach　a　semantics-robust　clustering　result.　A　case　study　on　clustering　a　sample　destination　set,　which　includes　13　Asia　Pacific　regions,　based　on　a　group　of　tourists＇　perceptions　is　also　proposed.