Spatial-temporal data analysis helps uncover value from data that moves through space and time. One such data analysis technique is clustering, which groups data based on a distance function to identify outliers or assist in classification tasks. Once spatial-temporal data is clustered with respect to space and time, cluster relationships can be observed, such as clusters entering or leaving another, merging, or splitting. A cluster lifetime describes the relationships that a given cluster had from its start to finish. The set of all cluster lifetimes that are related by the relationships describe a cluster dynamic. In this paper, we report our work in progress on a graph-based analysis approach to cluster lifetime dynamics. We discuss how cluster dynamics can be represented using graphs and the opportunities resulting from this approach, including visualization, graph pattern mining, graph classification, and graph compression. Enabled by graph-processing techniques, the proposed approach facilitates tasks such as the detection of regions of significant increase or decrease in the number of cluster elements (e.g. traffic jams), the calculation of a rise or decay parameter to describe this behavior for classification or comparison tasks, and the identification of a cluster’s lifetime, direction, and distance from or to a given point of interest.