A Streaming Algorithm for k-Means with Approximate Coreset

For computing the k-means clustering of the streaming and distributed big sparse data, we present an algorithm to obtain the sparse coreset for the k-means in polynomial time. This algorithm is mainly based on the explicit form of the center of mass and the approximate k-means. Because of the existence of the approximation, the coreset of the output inevitably has a factor, which can be controlled to be a very small constant.