The　misclassification　rate　can　be　used　to　measure　the　clustering　accuracy.　Cai　and　Zhang　(2018)　establish　an　upper　bound　of　misclassification　rate　for　the　two-class　clustering　model　Yi=mu　l(i)+Zi　is　an　element　of　R-p　where　li　is　an　element　of{-1,1}　and　Z(i)(i.i.d)similar　to　N(0,Ip),i　is　an　element　of{1,...,n},when　the　vector　dimension　p　is　larger　than　sample　size　n.　The　authors　prove　that　their　key　assumption　||mu||(2)＞=　C-gap(p/n)(1/4)　is　necessary　for　any　estimator　to　be　consistent.　This　paper　dis-cusses　the　same　problem　with　sub-Gaussian　noises　and　n　＞=　p:　We　first　use　Cai　and　Zhang＇s　method　to　give　an　upper　bound　of　the　misclassification　rate　for　||mu||(2)＞=　C-gap.Then　a　lower　bound　of　the　misclassification　rate　is　provided　under　some　technical　conditions,　which　matches　the　upper　bound　up　to　a　constant　multiple.　This　shows　our　upper　bound　estimation　optimal.　Examples　are　given　to　explain　those　technical　conditions　easily　satisfied.　Similar　to　Cai　and　Zhang＇s　work,　we　also　prove　the　assumption　||mu||(2)＞=　C-gap　in　our　upper　bound　estimation　necessary　for　any　estimator　to　be　consistent　as　well.　Finally,　numerical　simulations　support　our　theoretical　analysis.