I recently wrote a fun blog post (https://pncnmnp.github.io/blogs/odd-sketches.html https://pncnmnp.github.io/blogs/odd-sketches.html) about how to estimate Jac

I recently wrote a fun blog post (https://pncnmnp.github.io/blogs/odd-sketches.html https://pncnmnp.github.io/blogs/odd-sketches.html) about how to estimate Jaccard Similarity using min hashing, what b-bit min hashing is, and how to improve upon its limitations using a 2014 data structure called odd sketches. Jaccard Similarity's history is also quite interesting. From my blog: > In the late 19th century, the United States and several European nations were focused on developing strategies for weather forecasting, particularly for storm warnings. In 1884, Sergeant John Finley of the U.S. Army Signal Corps conducted experiments aimed at creating a tornado forecasting program for 18 regions in the United States east of the Rockies. To the surprise of many, Finley claimed his programs were 95.6% to 98.6% accurate, with some areas even achieving a 100% accuracy rate. Upon publishing his findings, Finley's methods were criticized by contemporaries who pointed out flaws in his verification strategies and proposed their solutions. This sparked a renewed interest in weather prediction, which is now referred to as the "Finley Affair." > One of these contemporaries was Grove Karl Gilbert. Just two months after Finley's publication, Gilbert pointed out that, based on Finley's strategy, a 98.2% accuracy rate could be achieved simply by forecasting no tornado warning. Gilbert then introduced an alternative strategy, which is now known as Jaccard Similarity. > So why is it named Jaccard Similarity? As it turns out, nearly three decades after Sergeant John Finley's tornado forecasting program in the 1880s, Paul Jaccard independently developed the same concept while studying the distribution of alpine flora.