Many　measures,　e.g.,　Maximal　Information　Coefficient　(MIC),　are　presented　to　identify　interesting　correlations　for　pairs　of　variables,　but　few　for　triplets　or　even　for　higher　dimension　variable　set.　Based　on　that,　the　Maximal　Information　Entropy　(MIE)　is　proposed　for　measuring　the　general　correlation　of　a　multivariable　data　set.　For　k　variables,　firstly,　the　maximal　information　matrix　is　constructed　according　to　the　MIC　scores　of　any　pairs　of　variables;　then,　maximal　information　entropy,　which　measures　the　correlation　degree　of　the　concerned　k　variables,　is　calculated　based　on　the　maximal　information　matrix.　The　simulation　experimental　results　show　that　MIE　can　detect　one-dimensional　manifold　dependence　of　triplets.　The　applications　to　real　datasets　further　verify　the　feasibility　of　MIE.　©,　2014,　Science　Press.　All　right　reserved.