Image　clustering　remains　challenging　when　handling　image　data　from　heterogeneous　sources.　Fusing　the　independent　and　complementary　information　existing　in　heterogeneous　sources　together　facilitates　to　improve　the　image　clustering　performance.　To　this　end,　we　propose　a　joint　learning　framework　of　multi-view　image　data　fusion　and　clustering　based　on　nuclear　norm　minimization.　Specifically,　we　first　formulate　the　problem　as　matrix　factorization　to　a　shared　clustering　indicator　matrix　and　a　representative　coefficient　matrix.　The　former　is　constrained　with　orthogonality　and　nonnegativity,　which　ensures　the　validation　of　clustering　assignments.　The　latter　is　imposed　with　nuclear　norm　minimization　to　achieve　compression　of　principal　components　for　performance　improvement.　Then,　an　alternating　minimization　strategy　is　employed　to　efficiently　decompose　the　multi-variable　optimization　problem　into　several　small　solvable　sub-problems　with　closed-form　solutions.　Extensive　experimental　results　on　real-world　image　and　video　datasets　demonstrate　the　superiority　of　proposed　method　over　other　state-of-the-art　methods.