We provide the first coreset for clustering points in $mathbb{R}^d$ that have multiple missing values (coordinates). Previous coreset constructions only allow one missing coordinate. The challenge in this setting is that objective functions, like $k$-Means, are evaluated only on the set of available (non-missing) coordinates, which varies across points. Recall that an $epsilon$-coreset of a large dataset is a small proxy, usually a reweighted subset of points, that $(1+epsilon)$-approximates the clustering objective for every possible center set. Our coresets for $k$-Means and $k$-Median clustering have size $(jk)^{O(min(j,k))} (epsilon^{-1} d log n)^2$, where $n$ is the number of data points, $d$ is the dimension and $j$ is the maximum number of missing coordinates for each data point. We further design an algorithm to construct these coresets in near-linear time, and consequently improve a recent quadratic-time PTAS for $k$-Means with missing values [Eiben et al., SODA 2021] to near-linear time. We validate our coreset construction, which is based on importance sampling and is easy to implement, on various real data sets. Our coreset exhibits a flexible tradeoff between coreset size and accuracy, and generally outperforms the uniform-sampling baseline. Furthermore, it significantly speeds up a Lloyds-style heuristic for $k$-Means with missing values.
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