Density-based　clustering　algorithms　are　well　known　for　identifying　clusters　possessing　very　different　local　densities　and　existing　in　different　regions　of　data　space.　However,　the　parameters　required　by　most　popular　density-based　clustering　algorithms,　such　as　DBSCAN,　are　hard　to　determine　but　have　significant　impacts　on　the　clustering　results.　In　this　paper,　we　present　a　new　density-based　clustering　algorithm　in　which　the　selection　of　appropriate　parameters　is　less　difficult　but　more　meaningful.　Experiments　performed　on　several　datasets　show　the　effectiveness　of　our　approach.　©　2017　IEEE.