In order to solve the problem that conditional constraints (context constraints) for personalized queries in database were not fully considered, a constraint model was proposed where the context i ^+≻i-| X means that the user prefers i ⁺ than i ^- based on the constraint of context X. Association rules mining algorithm based on MaxClique was used to obtain user preferences, and Conditional Preference Mining (CPM) algorithm combined with context obtained preference rules was proposed to obtain user preferences. The experimental results show that the context preference mining model has strong preference expression ability. At the same time, under the different parameters of minimum support, minimum confidence and data scale, the experimental results of preferences mining algorithm of CPM compared with Apriori algorithm and CONTENUM algorithm show that the proposed CPM algorithm can obviously improve the generation efficiency of user preferences.

In order to denoise image more effectively, an improved Contourlet transform denoising algorithm based on gray relational degree was proposed. On one hand, considering the gray relational degree and inter-scale from the high-frequency sub-band and low frequency sub-band by Contourlet transform, the Bayes threshold was improved; On the other hand, in order to achieve the purpose of adaptive denoising, the characteristics of Contourlet coefficients were used to improve the compromising threshold function. The experimental results show that the proposed algorithm can denoise image effectively, get higher PSNR and better visual quality, and has a good practicability.