In view of the inadequacy problem of most existing anomaly detection methods in effectively handling incomplete mixed data， a fuzzy multi-granularity anomaly detection algorithm for incomplete mixed data ADFIIS （Anomaly Detection in Fuzzy Incomplete Information System） was designed， which took into account the presence of missing values in both nominal and numeric attributes，and could handle mixed attribute data. The fuzzy similarity between attributes was defined and then the fuzzy entropy of each attribute was calculated. Based on the entropy values， a multi-granularity approach was employed to construct multiple attribute sequences. Subsequently，the outliers of each sample were calculated to characterize its degree of anomaly. Finally， the corresponding ADFIIS algorithm was designed， and its complexity was analyzed. Experiments were conducted on publicly available datasets， and the proposed algorithm was compared with some mainstream outlier detection algorithms such as ILGNI （Incomplete Local and Global Neighborhood Information network）. Experimental results show that ADFIIS has better Receiver Operating Characteristic （ROC） curve performance on incomplete mixed datasets. On average， the Area Under the ROC Curve （AUC） of ADFIIS is better than 90% of the comparison methods. Compared with ILGNI， which can also handle incomplete mixed data， the average AUC of ADFIIS is improved by 7 percentage points. In the proposed algorithm， the model expansion method is used to detect anomalies in incomplete datasets without changing the original datasets， which expands the application scope of anomaly detection.

Aiming at the problem that the existing evaluation methods do not take into account consumers' risk attitude, a subjective trust model based on consumers' risk attitude was proposed. Firstly, the historical information of entity evaluation attributes was converted into the interval number by using set-valued statistics theory. Then, by introducing risk attitude factor, the interval evaluation matrix was transformed into the evaluation matrix with risk attitude. Subsequently, the trust level of the entity was calculated by using the idea of relative closeness. Finally, the simulation results verify that the proposed method can make better trust decisions by considering risk attitude of consumers. The simulating experiment of anti-fraud further confirms the feasibility of the subjective trust model.

Traditional theory-deduced route choice always has large deviation from the actual one in complex rail transit network. The signaling data were collected from the passengers' mobile phone in rail wireless communication network. According to these data, a new travel route identification algorithm was proposed based on normal location update. Meanwhile, concerning the data missing, a repair algorithm was also put forward by using other signaling data of users to deduce their actual travel route by the K shortest paths. And the route validity would be checked to get the actual travel route. Finally, typical application in Beijing rail transit network was selected to validate this algorithm. The application results show that the algorithm has a good performance in illustrating the actual travelers' travel behaviors.