Focused on the issue that missing attribute values are obtained when an incomplete information system changes, in order to solve the problem of low time efficiency of updating the approximations in a multigranulation rough sets, a dynamic update algorithm based on tolerance relationship was proposed. Firstly, the properties of the approximations change based on tolerance relationship were discussed, and the change trends of the approximations of optimistic and pessimistic multigranulation rough sets were obtained according to the relevant properties. Then, a theorem of dynamic update tolerance class was proposed for the problem of low efficiency of updating tolerance class. Based on this, a dynamic update algorithm based on tolerance relationship was proposed. The simulation experiments were carried out using four data sets in UCI database. When the data set becomes larger, the calculation time of the proposed update algorithm is much smaller than that of the static update algorithm. The experimental results show that the time efficiency of the proposed dynamic update algorithm is higher than that of the static algorithm, which verifies the correctness and efficiency of the proposed algorithm.

Intrusion Detection System (IDS) has become an essential part of network security system, the practicability and durability of the existing intrusion detection methods still have improvement space, like detecting intrusion threats earlier and improving the detection accuracy of intrusion detection systems. Therefore, an intrusion detection method based on Ensemble Transfer Learning (ETL) via weighted mutual information was proposed. Firstly, the transfer strategy was used to model multiple feature sets, then the mutual information was used to measure the data attribution of feature sets under the transfer models in different domains. Finally, the weighted ensemble was performed to the multiple transfer models according to the measures, obtaining the ensemble transfer model. The method was able to construct the intrusion detection model better than the traditional models without ensemble or transfer learning by learning the knowledge of little labeled samples in the new environment and many labeled samples in the prior environment. The benchmark NSL-KDD dataset was used to evaluate the proposed method and the results show that the proposed method has good convergence performance and improve the accuracy of intrusion detection.

Community mining is an important application in the field of Web information mining. Topic detection and tracking is an important application in the field of text information study. Currently these two technologies are studied separately. To better apply these two technologies to complicated social networks formed by Internet, this paper combined them for research, discovered the relationships of community and topic, created static and dynamic interaction models for community mining and topic detection and tracking, and designed algorithms to mine communities, detect topics and track communities.