Tight lower bound instances for k-means++ in two dimensions

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The k-means++ seeding algorithm is one of the most popular algorithms that is used for finding the initial k centers when using the Lloyd's algorithm for the k-means problem. It was conjectured by Brunsch and Röglin [9] that k-means++ behaves well for datasets with small dimension. More specifically, they conjectured that the k-means++ seeding algorithm gives O(log⁡d) approximation with high probability for any d-dimensional dataset. In this work, we refute this conjecture by giving two dimensional datasets on which the k-means++ seeding algorithm achieves an O(log⁡k) approximation ratio with probability exponentially small in k. This solves open problems posed by Mahajan et al. [12] and by Brunsch and Röglin [9].