Feature　selection　is　an　effective　machine　learning　method　for　reducing　dimensionality,　removing　irrelevant　features,　increasing　learning　accuracy,　and　improving　result　comprehensibility.　However,　many　existing　feature　selection　methods　are　incapable　for　high　dimensional　data　because　of　their　high　time　complexity,　especially　wrapper　feature　selection　algorithms.　In　this　work,　a　fast　sequential　feature　selection　algorithm　(AP-SFS)　is　proposed　based　on　affinity　propagation　clustering.　AP-SFS　divides　the　original　feature　space　into　several　subspaces　by　a　cluster　algorithm,　then　applies　sequential　feature　selection　for　each　subspace,　and　collects　all　selected　features　together.　Experimental　results　on　several　benchmark　datasets　indicate　that　AP-SFS　can　be　implemented　much　faster　than　sequential　feature　selection　but　has　comparable　accuracies.　©　2013　IEEE.