Mining　frequent　itemsets　from　data　streams　has　extensively　been　studied,　and　most　of　them　focus　on　finding　complete　set　of　frequent　itemsets　in　a　data　stream.　Because　of　numerous　redundant　data　and　patterns　in　main　memory,　they　cannot　get　very　good　performance　in　time　and　space.　Therefore,　mining　frequent　closed　itemsets　in　data　streams　becomes　a　new　important　problem　in　recent　years,　where　algorithm　Moment　was　regarded　as　a　typical　method　of　them.　This　paper　presents　an　algorithm,　called　A-Moment,　which　uses　the　damped　window　technique,　approximate　count　method　and　distributed　updating　strategy　to　get　higher　mining　efficiency.　Experimental　results　show　that　our　algorithm　performs　much　better　than　the　previous　approaches.