We suggest efficient and provable methods to compute an approximation for imbalanced point clustering, that is, fitting k-centers to a set of points in \(\mathbb {R}^d\) , for any \(d,k\ge 1\) , where we aim to minimize the variance over the clusters. To this end, we utilize coresets, which, in the context of the paper, are essentially weighted sets of points in \(\mathbb {R}^d\) that approximate the fitting loss for every model in a given set, up to a multiplicative factor of \(1\pm \varepsilon \) . We provide experiments that show the empirical contribution of our suggested methods for real images (novel and reference), synthetic data, and real-world data. We also propose choice clustering, which, by combining clustering algorithms, yields better performance than each one separately.
