In　order　to　analyze　the　structure　of　a　data　set　simply　and　efficiently,　this　paper　proposes　a　new　clustering　algorithm　based　on　minimal　spanning　tree,　called　minimal　spanning　tree　cutting　algorithm　(MSTCA).　The　basic　idea　of　which　is　to　partition　a　data　set　into　subclasses　by　cutting　all　edges　whose　lengths　are　greater　than　a　certain　threshold　in　one　of　its　minimal　spanning　tree,　and　to　merge　those　relatively　small　subclasses　at　the　same　time.　MSTCA　can　guarantee　a　unique　clustering　result　without　considering　the　order　of　subclasses,　and　the　recursive　call　to　it　can　generate　a　hierarchical　structure　with　clusters　in　some　different　levels.　Computing　experiments　show　that　MSTCA　can　adaptively　choose　the　good　number　of　clusters　for　a　data　set　with　clusters　of　various　shapes　and　often　accurately　detect　reasonable　clusters　and　outliers　in　a　data　set　requiring　only　simple　selection　of　parameters.