Belief Peaks Clustering （BPC） algorithm is a new variant of Density Peaks Clustering （DPC） algorithm based on fuzzy perspective. It uses fuzzy mathematics to describe the distribution characteristics and correlation of data. However， BPC algorithm mainly relies on the information of local data points in the calculation of belief values， instead of investigating the distribution and structure of the whole dataset. Moreover， the robustness of the original allocation strategy is weak. To solve these problems， a fuzzy Clustering algorithm based on Belief Subcluster Cutting （BSCC） was proposed by combining belief peaks and spectral method. Firstly， the dataset was divided into many high-purity subclusters by local belief information. Then， the subcluster was regarded as a new sample， and the spectral method was used for cutting graph clustering through the similarity relationship between clusters， thus coupling local information and global information. Finally， the points in the subcluster were assigned to the class cluster where the subcluster was located to complete the final clustering. Compared with BPC algorithm， BSCC has obvious advantages on datasets with multiple subclusters， and it has the ACCuracy （ACC） improvement of 16.38 and 21.35 percentage points on americanflag dataset and Car dataset， respectively. Clustering experimental results on synthetic datasets and real datasets show that BSCC outperforms BPC and the other seven clustering algorithms on the three evaluation indicators of Adjusted Rand Index （ARI）， Normalized Mutual Information （NMI） and ACC.

With regard to the characteristics of randomness and fuzziness in service trust under computing environment, and lack of consideration in timeliness and recommend trust, a service trust evaluation method based on weighted multiple attribute cloud was proposed. Firstly, each service evaluation was given weight by introducing time decay factor, the evaluation granularity was refined by trust evaluation from multiple attribute of service, and direct trust cloud could be generated using the weighted attribute trust cloud backward generator. Then, the weight of recommender could be confirmed by similarity of evaluation, and recommended trust cloud was obtained by recommend information. Finally, the comprehensive trust cloud was obtained by merging direct and recommended trust cloud, and the trust rating could be confirmed by cloud similarity calculation. The simulation results show that the proposed method can improve the success rate of services interaction obviously, restrain malicious recommendation effectively, and reflect the situation of service trust under computing environment more truly.

Since most current attributes reduction algorithm can be only used for discrete decision tables, the correlation degree between condition attributes and decision attributes was defined as the importance degree of attributes, and meanwhile the overlap degree was defined based on the correlation degree and importance degree among attributes. The condition attributes' importance was renewed according to the overlap degree. To achieve the reduction of continuous-valued attributes set, an attributes reduction algorithm based on gray correlation analysis was proposed. The feasibility and effectiveness of the algorithm were verified in the simulation.