Published in AISTATS, 2019
$k$-means clustering is NP-hard in the worst case but previous work has shown efficient algorithms assuming the optimal $k$-means clusters are stable under additive or multiplicative perturbation of data. This has two caveats. First, we do not know how to efficiently verify this property of optimal solutions that are NP-hard to compute in the first place. Second, the stability assumptions required for polynomial time $k$-means algorithms are often unreasonable when compared to the ground-truth clusters in real-world data. A consequence of multiplicative perturbation resilience is center proximity, that is, every point is closer to the center of its own cluster than the center of any other cluster, by some multiplicative factor $ \alpha > 1 $.
We study the problem of minimizing the Euclidean $k$-means objective only over clusterings that satisfy $ \alpha $-center proximity. We give a simple algorithm to find the optimal $ \alpha $-center-proximal $k$-means clustering in running time exponential in k and $ 1/(\alpha−1) $ but linear in the number of points and the dimension. We define an analogous $ \alpha $-center proximity condition for outliers, and give similar algorithmic guarantees for $k$-means with outliers and $ \alpha $-center proximity. On the hardness side we show that for any $ \alpha’ > 1 $, there exists an $ \alpha \leq \alpha’ $, $ (\alpha > 1) $, and an $ \varepsilon_0>0 $ such that minimizing the $k$-means objective over clusterings that satisfy $ \alpha $-center proximity is NP-hard to approximate within a multiplicative $(1+\varepsilon_0)$ factor. Find the full paper here.
Recommended citation: Amit Deshpande, Anand Louis, Apoorv Singh ; Proceedings of Machine Learning Research, PMLR 89:2087-2095, 2019 http://proceedings.mlr.press/v89/deshpande19a.html