Time-series clustering by approximate prototypes
Ville Hautamäki, Pekka Nykanen, Pasi Fränti · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
Clustering time-series data poses problems, which do not exist in traditional clustering in Euclidean space. Specifically, cluster prototype needs to be calculated, where common solution is to use cluster medoid. In this work, we define an optimal prototype as an optimization problem and propose a local search solution to it. We experimentally compare different time-series clustering methods and find out that the proposed prototype with agglomerative clustering followed by k-means algorithm provides best clustering accuracy.