  Clustering is ubiquitous in data analysis, including analysis of time series.
It is inherently subjective: different users may prefer different clusterings
for a particular dataset. Semi-supervised clustering addresses this by allowing
the user to provide examples of instances that should (not) be in the same
cluster. This paper studies semi-supervised clustering in the context of time
series. We show that COBRAS, a state-of-the-art semi-supervised clustering
method, can be adapted to this setting. We refer to this approach as COBRAS-TS.
An extensive experimental evaluation supports the following claims: (1)
COBRAS-TS far outperforms the current state of the art in semi-supervised
clustering for time series, and thus presents a new baseline for the field; (2)
COBRAS-TS can identify clusters with separated components; (3) COBRAS-TS can
identify clusters that are characterized by small local patterns; (4) a small
amount of semi-supervision can greatly improve clustering quality for time
series; (5) the choice of the clustering algorithm matters (contrary to earlier
claims in the literature).
