# Clustering of events

I've a sequence of time ordered set of points: for each $$t=1...T$$ I have a set of points $$(x_{t,i},y_{t,i})$$. I need to cluster them together in space-time. I don't know however a priori the number of clusters. What approach do you suggest? My goal is to understand whether the event at time, let's say, $$t_1$$ belongs to some of other past cluster or forms a new one.

Why not adding time as a third dimension and use a standard clustering algorithm? I.e. create new "points" $$(x_i,y_i,t_i)$$ and use k-means or DBSCAN. With this way you can also use a custom scaling/normalization to adjust the weight of the the time dimension.