Distance　metric　learning　aims　to　learn　a　positive　semidefinite　matrix　such　that　similar　samples　are　preserved　with　small　distances　while　dissimilar　ones　are　mapped　with　big　values　above　a　predefined　margin.　It　can　facilitate　to　improve　the　performance　of　certain　learning　tasks.　In　this　article,　distance　metric　learning　and　clustering　are　integrated　into　an　unified　framework　via　rank-reduced　regression.　First,　distance　metric　learning　is　proved　to　be　consistent　with　rank-reduced　regression,　which　provides　a　new　perspective　to　learn　structured　regularization　matrices.　Second,　orthogonal　and　non-negative　rank-reduced　regression　problems　are　addressed　individually　for　clustering,　and　the　corresponding　algorithms　with　proved　convergence　are　proposed.　Finally,　both　distance　metric　learning　and　clustering　are　addressed　simultaneously　in　the　problem　formulation,　which　may　trigger　some　new　insights　for　learning　an　effective　clustering　oriented　low-dimensional　embedding.　To　show　the　superior　performance　of　the　proposed　method,　we　compare　it　with　several　state-of-the-art　clustering　approaches.　And,　extensive　experiments　on　the　test　datasets　demonstrate　the　superiority　of　the　proposed　method.