Mining changing regions from access-constrained snapshots: a cluster-embedded decision tree approach

Change detection on spatial data is important in many applications, such as environmental monitoring. Given a set of snapshots of spatial objects at various temporal instants, a user may want to derive the changing regions between any two snapshots. Most of the existing methods have to use at least one of the original data sets to detect changing regions. However, in some important applications, due to data access constraints such as privacy concerns and limited data online availability, original data may not be available for change analysis. In this paper, we tackle the problem by proposing a simple yet effective model-based approach. In the model construction phase, data snapshots are summarized using the novel cluster-embedded decision trees as concise models. Once the models are built, the original data snapshots will not be accessed anymore. In the change detection phase, to mine changing regions between any two instants, we compare the two corresponding cluster-embedded decision trees. Our systematic experimental results on both real and synthetic data sets show that our approach can detect changes accurately and effectively.