At the heart of both lossy compression and clustering is a trade-off between the fidelity and size of the learned representation. Our goal is to map out and study the Pareto frontier that quantifies this trade-off. We focus on the optimization of the Deterministic Information Bottleneck (DIB) objective over the space of hard clusterings. https://safeersappliancers.shop/product-category/faber-cappa-chloe-evo-110-0456-129-cooker-hood/