The　existing　rough　set　based　attribute　reduction　algorithms　are　mainly　designed　for　the　problem　of　the　underlying　data　residing　in　the　main　memory.　Therefore,　the　limitation　of　their　application　to　attribute　reduction　computation　of　huge　data　results　in　a　relatively　poor　scalability.　Inspired　by　supervised　learning　in　quest　(SLIQ)　algorithm,　a　specific　data　pre-processing　strategy　is　introduced　and　a　fast　attribute　reduction　algorithm　is　proposed　with　time　complexity　O(|U||C|).　The　experimental　results　show　that　the　proposed　algorithm　is　of　good　scalability.