The　learning　based　on　immune　principle　adapts　well　to　the　dynamic　environment,　and　thus　it　can　be　applied　to　the　data　stream　processing　which　is　dynamic　and　requires　high-speed　processing.　Therefore,　an　algorithm　of　clustering　data　streams　based　on　immune　principle　is　proposed,　namely　AIN-STREAM.　The　proposed　algorithm　can　track　the　evolving　clusters　on　noisy　data　sets.　AIN-STREAM　is　capable　of　adjusting　the　recognition　zone　of　B-cells　automatically　according　to　the　requirement　of　users　by　creating　and　maintaining　the　B-Cell　feature　vectors.　Thus,　the　stability　of　the　clustering　result　is　ensured.　Theoretical　analysis　and　comprehensive　experimental　results　demonstrate　that　AIN-STREAM　is　superior　over　other　immune　principle　based　clustering　algorithms　under　the　circumstance　of　similar　clustering　results.　Moreover,　the　results　show　that　AIN-STREAM　has　a　high　clustering　quality.