Affinity propagation（AP） clustering algorithm considers all clustering objects as potential clustering centers, and messages of responsibility and availability are exchanged between objects until a highquality set of clustering centers and corresponding clusters gradually emerge. But it is not appropriate for subspace clustering. To solve this problem, an entropy weighting AP algorithm for subspace clustering based on asynchronous granulation of attributes and samples (EWAP) is put forward through introducing the idea of granular computing into the affinity propagation clustering method. It removes the redundant attributes first, and then a step of modifying attribute weights is added to the clustering procedure for obtaining the exact weights value. At the end of iteration, the attribute weights of each subspace, an accurate result of attributes granularity and the corresponding clusters will be produced. The theory and practice prove that EWAP preserves the advantages of AP clustering and overcomes its shortage of unsatisfying subspace clustering.