Pith. sign in

REVIEW 1 cited by

SuperMinHash - A New Minwise Hashing Algorithm for Jaccard Similarity Estimation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1706.05698 v1 pith:GZPGJFSH submitted 2017-06-18 cs.DS

Otmar Ertl This is my paper · ORCID
classification cs.DS
keywords algorithmestimationjaccardsignaturessimilarityallowapproachbecause
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

This paper presents a new algorithm for calculating hash signatures of sets which can be directly used for Jaccard similarity estimation. The new approach is an improvement over the MinHash algorithm, because it has a better runtime behavior and the resulting signatures allow a more precise estimation of the Jaccard index.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sampling-Based Estimation of Jaccard Containment and Similarity

    stat.CO 2025-07 conditional novelty 4.0 of 10

    A binomial approximation to the sample overlap likelihood yields a simple estimator for Jaccard containment, but several of the paper's error bounds and sample size formulas are not rigorously supported.

Pith tools