We introduce an approach for the segmentation, visualization and tracking of regions of interest in large scale tensor field datasets generated by computational turbulent combustion simulations. We use canopy clustering followed by a K-means algorithm to partition and cluster the tensor field components. The resulting clusters are tracked through multiple timesteps. Interactive, hardware-accelerated volume renderings are generated using the cluster indices. Results on two rich datasets show this approach can assist in the visual analysis of combustion tensor fields.