Support vector domain classifiers have disadvantages like long training time and large memory. The clustering piecewise double support vector domain classifier(CPDSVDC) is proposed. CPDSVDC uses C means algorithm to partition the original space, and selects the initial cluster centers by samples with large density indexes. The dual support vector domain classifier is constructed in each divided subspace, and the corresponding piecewise decision function is also constructed based on the position relationship between the test sample and the two minimum enclosing spheres. The variable distance of the test sample is defined, and linking rule is used to combine classification results in all subspaces. Numerical experiments demonstrate that the CPDSVDC has high classification accuracy that varies slightly with parameters and low training time that decreases with the number of subspaces.