Mining changing regions from access-constrained data sets: A cluster-embedded decision tree approach

摘要

Change detection is important in many applications. 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, the original data may not be available for change detection. In this work, we tackle the problem by proposing a simple yet effective model-based approach. In the model construction phase, original data sets are summarized using the novel cluster-embedded decision trees as concise models. Once the models are built, the original data will not be accessed anymore. In the change detection phase, to compare any two data sets, 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.