Link model is able to model community detection problem on networks. Compared with other similar models including symmetric models and conditional models, PPL (Popularity and Productivity Link) deals more types of networks, and detects communities more accurately. But PPL is an unsupervised model, and works badly when the network structure is unclear. In addition, PPL is not able to utilize priors that are easily captained. In order to improve its performance by using as less as possible, an Active Node Prior Learning (ANPL) algorithm was provided. ANPL selected the highest utility and easily labeled pairwise constraints, and generated automatically more informative labeled nodes based on the labeled pairwise constraints. Based on the PPL model,a Semi-supervised PPL (SPPL) model was proposed for community detection, which combined the topology of network and node labels learned from the ANPL algorithm. Experiments on synthetic and real networks demonstrate that using node priors from the ANPL algorithm and the topology of a network, SPPL model excels to unsupervised PPL model and popular semi-supervised community detection models based on Non-negative Matrix Factorization (NMF).

In order to improve the level of assessment of the credit risk of commercial bank credit management system based on data mining, the model of multiple decision trees by Choquet fuzzy integral fusion (MTCFF) was applied to the system. The basic idea was to mine the classified customer data by decision tree, form the different decision trees and rules according to different pruning degree, and detect unclassified customer data by different decision tree rules, and then nonlinearly combine the results from multiple decision trees by Choquet fuzzy integral to get the best decision. Using the German of the UCI dataset, the experimental results show that fusion of Choquet fuzzy integral is superior to the single decision tree in terms of classification accuracy, and it is also superior to other linear fusion methods. Choquet fuzzy integral is superior to Sugeno fuzzy integral.