In the super-peer network, the super-peer topology structure and its mechanism of dynamic maintenance and search routing are important factors affecting network performance and search efficiency. In this paper, a new structure named k-Perfect Difference Graph (PDG) was proposed by analyzing the characteristics and the deficiencies of PDG, new Super-peer Network based on k-PDG (KPDGN) was constructed, and then the mechanism of dynamic maintenance and search routing was presented in KPDGN. The analysis and simulation results show that compared with current supper-peer topology, KPDGN has good performance with constant degree and fixed adjacent nodes, which reduces the bandwidth consumption during searching and the cost of topology construction and maintenance.

With diversification analyses of lost customers in applications, a multi-mode classification algorithm was presented. It applied clustering algorithm to segment the lost customers into many subgroups, then used classification algorithm to build the classifying models on each subgroup. Meanwhile, it filtered the low-precision class models to ensure the precision improvement of forecast lists. Compared with Logistic, decision tree and neural network, the experimental results show that the new classification algorithm has better performance.