An Algorithm for Online K-Means Clustering
نویسندگان
چکیده
This paper shows that one can be competitive with the kmeans objective while operating online. In this model, the algorithm receives vectors v1, . . . , vn one by one in an arbitrary order. For each vector vt the algorithm outputs a cluster identifier before receiving vt+1. Our online algorithm generates Õ(k) clusters whose k-means cost is Õ(W ∗) where W ∗ is the optimal k-means cost using k clusters.1 We also show that, experimentally, it is not much worse than k-means++ while operating in a strictly more constrained computational model.
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