How do you design clustering systems that scale to billion-scale datasets?

Billion-scale clustering uses distributed processing, sampling, and approximate algorithms like mini-batch K-Means.

At billion-scale, full clustering is computationally infeasible. Systems use sampling strategies, distributed computation frameworks, and approximate clustering methods. Mini-batch K-Means processes data in chunks, while hierarchical summarization reduces dataset size iteratively. Embedding compression and ANN search are also used to reduce complexity.