An　algorithm　based　on　immune　principle,　named　IUMicro,　is　proposed　to　cluster　uncertain　data　streams.　IUMicro　applies　a　dynamically　updated　immune　model　to　adapt　to　the　data　streams.　An　effective　B-cell　feature　vector　and　updating　strategy　are　used　to　collect　statistical　information　of　data　streams　on　line　by　this　model.　To　choose　the　optimal　candidate　cluster　for　each　increasing　tuple　in　the　data　stream,　IUMicro　defines　a　probability　radius　of　a　B-cell＇s　recognition　zone　to　address　both　uncertainty　and　distance　metric.　The　offline　clustering　is　an　arbitrary-shape　unsupervised　clustering　based　on　immune　B-cells＇　spatial　relationship　between　regions.　The　experimental　results　show　that　IUMicro　effectively　suppresses　noise　and　gains　better　clustering　quality　at　a　high　processing　speed.