In spontaneous speech, Mandarin tones that belong to the same tone category may exhibit many different contour shapes. We explore the use of time-series data mining techniques for understanding the variability of tones in a large corpus of Mandarin newscast speech. First, we adapt a graph-based approach to characterize the clusters (fuzzy types) of tone contour shapes observed in each tone n-gram category. Second, we show correlations between these realized contour shape clusters and a bag of automatically extracted linguistic features. We discuss the implications of the current study within the context of phonological and information theory.