I'm working on time series in the scope of similarity detection at the moment. What seems to be a well researched approach is dynamic time warping in combination with k-1NN as classification algorithmn.
If I am right, the Dynamic Time Warping Distance is used to calculate the distance between two/a set of series. Afterwards k-1NN is applied. K-1NN takes the distance and returns for every time series the closest series.
Another approach is to cluster the series using their pair wise distance as input for an hierarchical clustering algorithm.
It seems to me, that both HAC and k-1NN are doing the exact same thing. Both are suited for unlabeld data and can return the closest point of the data set.
Am I missing something and the link between them is so obvious, that nobody wrote that simply down?