Common forms of eye-tracking analysis convey a subject's attention by grouping dense aggregations of gaze points into areas of interest. These areas of interest are often corroborated against pictorial figures such that the investigator can infer correlation of a subject's attention with a depicted object in a scene. However, while it is tempting to associate viewer interest with the images that underlay them, the identification of what constitutes legitimate pictorial elements can be contingent upon personal judgment, and so can differ drastically between subjects and the investigator. Accordingly, this research proposes an approach in which areas of interest are determined by data-driven methods using subject-specific gaze point information. The initial step of our method involved finding related clusters using a spatial cluster algorithm, and thereafter, each resultant group of gaze points was graphically outlined using a concave hull algorithm. We defined various typologies that describe the resolution of areas of interest both spatially and temporally.