MinHash classic: clustering huge datasets with a KV store in five lines
moultano · x · 2026-09-07
Ryan Moulton's classic technical article explains how to cluster massive—or even infinite streaming—datasets using nothing more than a key-value store and a few lines of code.
- Core idea: build a locality-sensitive hash (LSH) so similar items share a key with high probability, letting you assign clusters in a single pass without revisiting data.
- Starting from set similarity and the Jaccard coefficient, the article derives MinHash: hashing items into (0,1] doubles as a similarity key and, as the author notes, a locality-sensitive hash of the distribution itself.
- C++ implementations accompany each algorithm, with discussion of multi-hash/banding tricks to approximate the ideal clustering function.
- Fits cases where only simple structures scale, MapReduce is overkill, or data can be inspected exactly once.
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