In　this　paper,　we　propose　a　novel　density-based　radar　scanning　clustering　algorithm.　Its　main　objective　is　to　quickly　discover　and　accurately　extract　individual　clusters　by　employing　the　radar　scanning　strategy.　By　using　this　algorithm,　the　number　of　clusters　does　not　need　to　be　specified　beforehand.　Two　techniques　are　utilized　in　our　proposed　method.　First,　we　use　a　fast　mean-shift　algorithm　with　adaptive　radius　and　active　subsets　to　effectively　locate　the　centers,　reducing　the　computational　time　significantly.　Second,　we　employ　the　shape　of　the　probability　density　function　of　the　distribution　of　distances　between　a　selected　point　and　the　other　points　in　the　data　set.　This　is　performed　to　determine　the　critical　parameters　of　the　radiuses　of　the　fast　mean-shift　algorithm　and　radiuses　of　clusters.　The　new　algorithm　has　four　merits.　It　reduces　the　computational　complexity,　overcomes　problems　caused　by　high　dimensionality,　is　capable　of　dealing　with　heterogeneous　spherical　data　sets,　and　lastly,　is　robust　to　noise　and　outliers.　After　applying　our　proposed　method　to　several　kinds　of　synthetic　and　real-world　data　sets,　the　results　indicate　that　the　density-based　radar　scanning　algorithm　is　efficient　and　accurate.