Time-series Clustering by Approximate Prototypes

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.