We　revisit　the　classical　metric　k-median/means　with　outliers　in　this　paper,　whose　proposal　dates　back　to　(Charikar,　Khuller,　Mount,　and　Narasimhan　SODA’01).　Though　good　approximation　algorithms　have　been　proposed,　referring　to　the　state-of-the-art　(6.994+　Ε　)-approximation　(Gupta,　Moseley　and　Zhou　ICALP’21)　for　k-median　with　outliers　and　(53.002+　Ε　)-approximation　(Krishnaswamy,　Li,　and　Sandeep　SODA’18)　for　k-means　with　outliers　respectively,　we　are　interested　in　finding　efficient　fpt　(fixed-parameter　tractable)　approximations,　following　a　recent　research　mainstream　for　constrained　clusterings.　As　our　main　contribution,　we　propose　a　simple　but　efficient　technical　framework　that　yields　a　(3　+　Ε)　/　(9　+　Ε)　-approximation　for　k-median/means　with　outliers,　albeit　in　((m+　k)　/　Ε)　O(k)·　nO(1)　time.　It　is　notable　that　our　results　match　with　previous　result　(Goyal,　Jaiswal,　and　Kumar　IPEC’20)　in　terms　of　ratio　and　asymptotic　running　time.　But　as　aforementioned,　our　technique　is　much　more　simplified　and　straightforward,　where　instead　of　considering　the　whole　client　set,　we　restrict　ourselves　to　finding　a　good　approximate　facility　set　for　coreset,　which　can　be　done　easily　in　fpt　time　even　with　provably　small　loss.　Similar　idea　can　be　applied　to　more　constrained　clustering　problems　whose　coresets　have　been　well-studied.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2024.