Scalable Active Temporal Constrained Clustering

We introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large temporal data. It consists of a constrained clustering algorithm which optimizes the clustering quality, constraint violation and the historical cost between consecutive data snapshots. At the center of our framework is a simple yet effective active learning technique for iteratively selecting the most informative pairs of objects to query users about, and updating the clustering with new constraints. Those constraints are then propagated inside each snapshot and between snapshots via constraint inheritance and propagation to further enhance the results. Experiments show better or comparable clustering results than existing techniques as well as high scalability for large datasets.