As　a　ubiquitous　method　in　the　field　of　machine　learning,　clustering　algorithm　attracts　a　lot　attention.　Because　only　some　basic　information　can　be　utilized,　clustering　data　points　into　correct　categories　is　a　critical　task　especially　when　the　cluster　number　is　unknown.　This　paper　presents　an　algorithm　which　can　find　the　cluster　number　automatically.　It　firstly　constructs　hyper-planes　based　on　the　marginal　of　sample　points.　Then　an　adjacent　relationship　between　data　points　is　defined.　Based　on　it,　connective　components　are　derived.　According　to　a　validity　index　proposed　in　this　paper,　the　high-qualified　connective　components　are　selected　as　cluster　centers.　Meanwhile,　the　clusters＇　number　is　also　determined.　Another　contribution　of　this　paper　is　that　all　the　parameters　in　this　algorithm　can　be　set　automatically.　To　evaluate　its　robustness,　experiments　on　different　kinds　of　benchmark　datasets　are　carried　out.　They　show　that　the　performances　are　even　better　than　some　other　methods＇　best　results　which　are　selected　manually.