This chapter studies algorithms for analyzing trajectory data. Section 10.1 addresses trajectory simplification and compression. Trajectory data can be voluminous with several thousand points in a single trajectory, hindering analysis tasks. The goal of trajectory simplification is to reduce the number of points in the trajectory while preserving the original trajectory features. Section 10.2 studies trajectory segmentation, which aims at splitting the trajectories into individual trips. Since the notion of a trip is application-dependent, we present various ways of splitting trajectories that can be adapted to most scenarios. Section 10.3 addresses heat maps, a common visual analytics technique for understanding the distribution of moving objects in space and time and for identifying hotspots. We continue with Sect. 10.4, which studies trajectory similarity with three commonly used similarity measures, namely, dynamic time warp (DTW), Fréchet distance, and time warp edit distance (TWED). The second part of this chapter deals with clustering using the mechanisms introduced in the sections described above. In Sect. 10.5 we study classic spatial and temporal clustering, using the NYC Citi Bike dataset used in Chap. 8. We review three different kinds of clustering, namely, distance-based, density-based, and agglomerative clustering. We also review the notion of dimensionality reduction. Finally, in Sect. 10.6 we study trajectory clustering using the AIS dataset and applying the simplification and segmentation algorithms prior to the clustering analysis itself.