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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SuperMinHash - A New Minwise Hashing Algorithm for Jaccard Similarity Estimation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1706.05698/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1706.05698/feed.xml" rel="self" type="application/rss+xml" /><item><title>SuperMinHash - A New Minwise Hashing Algorithm for Jaccard Similarity Estimation — mentioned by samscully</title><link>https://news.ycombinator.com/item?id=35229623</link><guid isPermaLink="false">urn:hackerpapers:mention:1706.05698:35229623</guid><pubDate>Mon, 20 Mar 2023 10:34:36 GMT</pubDate><description>&lt;p&gt;The book Mining of Massive Datasets [1] has useful information on building an efficient similarity index using Jaccard/minhash. I would also recommend Otmar Ertl&amp;#x27;s papers on extensions of minhash that approximate Jaccard better in certain situations, e.g. superminhash [2]. [1] http://www.mmds.org/ Chapter 3 [2] https://arxiv.org/abs/1706.05698&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1706.05698/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DS</category></item></channel></rss>
