eSeeTrack, an eye-tracking visualization, facilitates exploration and comparison of sequential gaze orderings in a static or a dynamic scene. It extends current eyetracking data visualizations by extracting patterns of sequential gaze orderings, displaying these patterns in a way that does not depend on the number of fixations on a scene, and enabling users to compare patterns from two or more sets of eye-gaze data. Extracting such patterns was difficult, if not impossible, with previous visualization techniques. eSeeTrack combines a timeline and a tree-structured visual representation to embody three aspects of eye-tracking data that users are interested in: duration, frequency and orderings of fixations. eSeeTrack allows ordering of fixations to be rapidly queried, explored and compared. Two case studies on surgical simulation and retail store chain to assert the capabilities of eSeeTrack are discussed in this thesis. Furthermore, eSeeTrack provides an effective and efficient mechanism to determine the pattern outliers. This approach can be effective for behavior analysis in a variety of domains that are also described.