Clustering　algorithms　aim　at　gathering　similar　data　points　from　a　dataset　in　an　unsupervised　manner.　Although　the　batch　clustering　algorithms　have　relatively　high　accuracy,　they　cannot　make　use　of　the　dynamic　clustering　results　efficiently.　The　requirement　of　using　the　whole　dataset　in　calculation　results　in　the　problems　of　resource　waste　and　high　time　cost.　On　the　contrary,　incremental　clustering　only　needs　to　update　the　varied　part　of　a　model　upon　the　arrival　of　new　data,　which　makes　it　unnecessary　to　recluster　the　whole　data　all　the　time.　The　feature　is　very　suitable　for　the　streaming　data　process,　but　it　decreases　the　accuracy　of　the　algorithms　and　cannot　satisfy　the　low　latency　requirement　of　real-time　data　processing.　In　response　to　this　problem,　the　paper　proposes　a　novel　unified　batch　and　streaming　clustering　model　(UBSCM)　based　on　streaming　computation,　which　includes　a　streaming　cluster　feature　updating　mechanism　(SCFUM).　The　Flink　framework　is　used　to　implement　a　new　streaming　KMeans　algorithm　based　on　UBSCM　(KMeansUBSP).　The　experiments　on　the　real-world　datasets　validate　that　the　new　streaming　KMeans　algorithm　is　effective　in　clustering　the　batch　and　streaming　data　in　a　unified　manner.　©　2021,　Springer　Nature　Singapore　Pte　Ltd.