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An ensemble clustering model for mining concept drifting stream data in emergency management

Mining data streams with concept drifts is always an important and challenge task for researchers in both application and theory areas, such as emergency management. Because of requiring massive training data with labels, it is a hard and time costing work for existing (ensemble) classical models, sometimes even impossible. Aim to resolve this issue, in this paper; we propose an ensemble clustering model for mining concept drifting stream data in emergency management. Motivated by classifiers, the model will mine the data in two steps: "training" and "testing", just with a small training set. According to the experiment, the results demonstrate the effect and performance of the proposed model in mining data streams with concept drifts.