Traditional clustering algorithms aim to certain data in general, which cannot solve the clustering problem for uncertain data. The existing density-based clustering algorithms for uncertain data have the problems that parameters are too sensitive and the computational efficiency is low. Therefore, an algorithm, named optimal ??-nearest neighbors and local density-based clustering algorithm for uncertain data(OLUC), is proposed to solve the clustering problem for uncertain data by introducing concepts of new dissimilarity function for uncertain data, optimal ??-nearest neighbors, local density and mutual inclusion relation. The algorithm not only can reduce the sensitivity of parameters and improve the computational efficiency, but also has the abilities of optimizing ??-nearest neighbors in the dynamic adaptive way, deciding cluster center quickly and optimizing denoising. The experimental results show that the algorithm is effective on clustering for uncertain data whatever with noise or without noise, and achieves good results.