I'm trying to cluster time series of different length and I came up to an idea to use DTW as a similarity measure, which seems to be adequate, but the thing is, I cannot use it with K-means, since it's hard to define centroids based on time series which can have different length/phase. So I was thinking about Hierarchical clustering, since it seems appropriate to combine with DTW, but it's not scalable. So my next thought is to try with bisecting k-means that seems scalable, since it is based on K-means step repetitions. My idea is next, by steps:
- Take two signals as initial centroids (maybe two signals that have smallest similarity, calculated using DTW)
- Assign all signals to two initial centroids
- Repeat the procedure on the biggest cluster
In this way I could use DTW as distance measure, that could be useful since my data may be shifted, skewed, and avoid calculating centroids. At the end I could take one signal from each cluster that is the most similar with others in cluster (some kind of centroid/medioid).
What do you think about this approach and about the scalability?