  Scaling clustering algorithms to massive data sets is a challenging task.
Recently, several successful approaches based on data summarization methods,
such as coresets and sketches, were proposed. While these techniques provide
provably good and small summaries, they are inherently problem dependent - the
practitioner has to commit to a fixed clustering objective before even
exploring the data. However, can one construct small data summaries for a wide
range of clustering problems simultaneously? In this work, we affirmatively
answer this question by proposing an efficient algorithm that constructs such
one-shot summaries for k-clustering problems while retaining strong theoretical
guarantees.
