I have a dataset with vectors in 2-dimensional space that form separate sequences (paths). Full data is presented below: , while a random sample of 5 paths looks like below (please note that incontinuity in paths are natural for the data and doesn't mean missing values):
I would like to find similar paths, where similar would mean (in order from the most to the less prominent):
- they end up in a similar region
- they are similar in direct length (i.e. length from start to end on x axis)
- they are similar in complexity (i.e. number of vectors)
- they starts in a similar region
What clustering algorithms are natural choice for such a setup? What are things worth to be aware while clustering paths? How can I deal with the fact, that different paths has different number of vectors? How can I represent a data to take that into account?