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Improving the Sensitivity of MinHash Through Hash-Value Analysis

Authors Gregory Kucherov , Steven Skiena

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Author Details

Gregory Kucherov
  • LIGM, CNRS/Université Gustave Eiffel, Marne-la-Vallée, France
Steven Skiena
  • Dept. of Computer Science, Stony Brook University, Stony Brook, NY, USA

Funding

  • Skiena, Steven: Partially supported by a travel grant from Université Paris-Est, and NSF grant IIS-1927227.

Cite AsGet BibTex

Gregory Kucherov and Steven Skiena. Improving the Sensitivity of MinHash Through Hash-Value Analysis. In 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 259, pp. 20:1-20:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CPM.2023.20

Abstract

MinHash sketching is an important algorithm for efficient document retrieval and bioinformatics. We show that the value of the matching MinHash codes convey additional information about the Jaccard similarity of S and T over and above the fact that the MinHash codes agree. This observation holds the potential to increase the sensitivity of minhash-based retrieval systems. We analyze the expected Jaccard similarity of two sets as a function of observing a matching MinHash value a under a reasonable prior distribution on intersection set sizes, and present a practical approach to using MinHash values to improve the sensitivity of traditional Jaccard similarity estimation, based on the Kolmogorov-Smirnov statistical test for sample distributions. Experiments over a wide range of hash function counts and set similarities show a small but consistent improvement over chance at predicting over/under-estimation, yielding an average accuracy of 61% over the range of experiments.

Subject Classification

ACM Subject Classification
  • Theory of computation → Bloom filters and hashing
Keywords
  • MinHash sketching
  • sequence similarity
  • hashing

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