An unsupervised anomaly detection approach DGANs is proposed based on ensemble GANs with Dropout. The comparisons with representative approaches on 10 public datasets show it has advantages in accuracy, recall and F1 scores. DGANs can address the overfitting problem in ensemble GANs training on high-dimensional datasets.

Psychological Attention (PA) is introduced to characterize multivariate campus behaviors of university students, and a computation model for PA qualities is proposed driven by online behavioral big data. The PA-based behavior clustering is applied into the analytics of multivariate behaviors of university students to reveal the impacts of PA qualities on academic performances. Experiments show PA-based behavior clustering has great advantages in the Silhouette Coefficient (SC), Davies-Bouldin Index (DBI), and Calinski-Harabasz index (CHI) over the clustering based on the traditional features. It means that PA qualities can well distinguish the potential patterns in multivariate behaviors, since the students in one cluster have a higher possibility of 38.10% to get the top-level scholarship than those in another one. Experimental results also represents that the Stability and Distribution of the PA in multivariate behaviors have active impacts on students’ academic performances. The studies in the paper can be applied into the prediction of students’ academic performances, and the personalized in-advance guidances from them.