To overcome the instability of one single clustering result,we propose a new clustering ensemble method based on Dempster-Shafer theory (also known as belief function theory).In general,ensemble methods consist of two principal steps:generating base partitions and combining them into a single one;our method mainly focuses on the second step.After obtaining the base partitions in the first step,we convert them into an intermediate interpretation,which can be called a relational representation.We believe that the evidence source from the relational representations may be doubtful,which can be fixed by using the discounting process in belief function theory.After discounting the relational representations,we can combine them in the evidential level by different combination rules.Then,we can obtain the belief matrix or plausibility matrix from the fused relational representation,which can be seen as a co-association matrix between objects.To make full use of the transitive property between objects,we treat this co-association matrix as a fuzzy relation and make it the transitive closure to yield a fuzzy equivalence relation.The final partition is obtained by applying some clustering algorithms to the new co-association matrix.The experimental results show the stability and efficiency of our method.