Trajectory Clustering: Which Clustering Method?

Question

As a newbie in Machine Learning, I have a set of trajectories that may be of different lengths. I wish to cluster them, because some of them are actually the same pa

Answer 1

Every clustering algorithm needs a metric. You need to define distance between your samples. In your case simple Euclidean distance is not a good idea, especially if the trajectories can have different lengths.

If you define a metric, than you can use any clustering algorithm that allows for custom metric. Probably you do not know the correct number of clusters beforehand, then hierarchical clustering is a good option. K-means doesn't allow for custom metric, but there are modifications of K-means that do (like K-medoids)

The hard part is defining distance between two trajectories (time series). Common approach is DTW (Dynamic Time Warping). To improve performance you can approximate your trajectory by smaller amount of points (many algorithms for that).