Provable Imbalanced Point Clustering

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 Rᵈ, for any d,k≥ 1. To this end, we utilize coresets, which, in the context of the paper, are essentially weighted sets of points in Rᵈ that approximate the fitting loss for every model in a given set, up to a multiplicative factor of 1±ε. We provide [Section 3 and Section E in the appendix] 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.