Feature　clustering　is　a　powerful　technique　for　dimensionality　reduction.　However,　existing　approaches　require　the　number　of　clusters　to　be　given　in　advance　or　controlled　by　parameters.　In　this　paper,　by　combining　with　affinity　propagation　(AP),　we　propose　a　new　feature　clustering　(FC)　algorithm,　called　APFC,　for　dimensionality　reduction.　For　a　given　training　dataset,　the　original　features　automatically　form　a　bunch　of　clusters　by　AP.　A　new　feature　can　then　be　extracted　from　each　cluster　in　three　different　ways　for　reducing　the　dimensionality　of　the　original　data.　APFC　requires　no　provision　of　the　number　of　clusters　(or　extracted　features)　beforehand.　Moreover,　it　avoids　computing　the　eigenvalues　and　eigenvectors　of　covariance　matrix　which　is　often　necessary　in　many　feature　extraction　methods.　In　order　to　demonstrate　the　effectiveness　and　efficiency　of　APFC,　extensive　experiments　are　conducted　to　compare　it　with　three　well-established　dimensionality　reduction　methods　on　14　UCI　datasets　in　terms　of　classification　accuracy　and　computational　time.