How to reduce the impact of uncertain data on high dimensional data clustering is the difficulty of current research. Aiming at the problem of low clustering accuracy caused by uncertain data and curse of dimensionality, the method of determining the uncertain data and then clustering the certain data was adopted. In the process of determining the uncertain data, uncertain data were divided into value uncertain data and dimension uncertain data, and were processed separately to improve algorithm efficiency. K-Nearest Neighbor ( KNN) query combined with expected distance was used to obtain the approximate value of uncertain data with the least impact on the clustering results, so as to improve the clustering accuracy. After determining the uncertain data, the method of subspace clustering was adopted to avoid the impact of the curse of dimensionality. The experimental results show that high-dimensional uncertain data clustering algorithm based on Clique for Uncertain data (UClique) has good performance on UCI datasets, has good anti-noise performance and scalability, can obtain better clustering results on high dimensional data, and can achieve the experimental results with higher accuracy on different uncertain datasets, showing that the algorithm is robust and can effectively cluster high dimensional uncertain data.

The solution of membership problem is essential to design an available algorithm of scheme decomposition. Because of the partial order among temporal types in strong partial ordered temporal scheme, it is difficult to solve its membership problem. The concepts of mixed dependency base on given temporal type, mixed dependency base in strong partial ordered scheme, mixed set closure of partial temporal functional dependency and temporal multi-valued dependency and mixed closure of strong partial ordered scheme were given. The algorithms of dependency base of attribution and closure of attribution sets were also given. On this basis, the algorithm of membership problem of mixed dependency set in strong partial ordered scheme was put forward. The proof for its termination, correction and time complexity were presented. Application examples show that the research on related theory and algorithm solves determination of the membership problem in strong partial ordered mixed dependencies, and provides a theoretical basis for solving the strong partial order temporal scheme and the design of temporal database standardization.