In this paper, we proposed an unsupervised machine-learning-based framework to automate the process of extracting suspicious gamma dose rate incidents from the real unlabeled raw historical data measured in the German Radiation Early Warning Network and identify the underlying events behind each. This raised the research problem of clustering unlabeled time series data with varying lengths and scales. Based on the many evaluations, we demonstrated that the state-of-the-art’s most popular time series clustering models were not suitable to perform this task. This motivated us to introduce our own approach. Through this approach we were able to perform online classification for gamma dose rate incidents of varying lengths and scales.