The reasons for concept drift in a data stream can vary widely, from deterioration of a machine to a change in peoples' buying patterns. In order to effectively detect concept drifts, most predictive stream mining systems contain a drift detector that monitors and signals concept drifts. However, few of these systems are designed to find drifts in transactional datasets, which have unlabelled data. Transactional datasets describe events, such as orders or payments, which are traditionally analysed using association rules. In this paper, we propose a novel drift detection technique, ProChange, that has two parts. The first part is a drift detector, VR-Change, that finds both real and virtual drifts in unlabelled transactional data streams using the Hellinger distance. The second part is a drift predictor, which models the volatility of drifts using a probabilistic network to predict the location of future drifts. Using the predictor, we can dynamically adapt the confidence threshold, enabling VR-Change to be more sensitive around potential future drift points. We evaluated the performance of ProChange by comparing it against traditional detectors showing that it detects both real and virtual drifts effectively and efficiently in terms of accuracy.
