Ant-SNE: Tracking Community Evolution via Animated t-SNE

摘要

We introduce a method for tracking the community evolution and a prototype (Ant-SNE) for analyzing multivariate time series and guiding interactive exploration through high-dimensional data. The method is based on t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for nonlinear dimension reduction well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. By tracking the evolution of temporal multivariate data points, we are able to locate unusual behaviors (outliers) and interesting sub-series for further analysis. In the experiments, we conducted two case studies with the US employment dataset and the HPC health status dataset in order to confirm the effectiveness of the proposed system.