A Review for Weighted MinHash Algorithms (Extended abstract)

Wu, W; Li, B; Chen, L; Gao, J; Zhang, C

A Review for Weighted MinHash Algorithms (Extended abstract)

Wu, W Li, B Chen, L

Gao, J Zhang, C

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023, 2023-April, pp. 2553-2573
Issue Date:: 2023-07-26

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Field	Value	Language
dc.contributor.author	Wu, W
dc.contributor.author	Li, B
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729
dc.contributor.author	Gao, J
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154
dc.date	2023-04-03
dc.date.accessioned	2023-11-13T03:42:59Z
dc.date.available	2023-11-13T03:42:59Z
dc.date.issued	2023-07-26
dc.identifier.citation	2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023, 2023-April, pp. 2553-2573
dc.identifier.isbn	979-8-3503-2227-9
dc.identifier.issn	1063-6382
dc.identifier.issn	2375-026X
dc.identifier.uri	http://hdl.handle.net/10453/173371
dc.description.abstract	Data similarity computation is a fundamental research topic which underpins many high level applications based on similarity measures However the exact similarity computation has become daunting in large scale real world scenarios Currently MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore weighted MinHash is utilized to estimate the generalized Jaccard similarity of weighted sets This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms Also we have developed a Python toolbox for the algorithms and released it in our github
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP180100966
dc.relation.ispartof	2023 IEEE 39th International Conference on Data Engineering (ICDE)
dc.relation.ispartof	IEEE International Conference on Data Engineering
dc.relation.isbasedon	10.1109/icde55515.2023.00319
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Review for Weighted MinHash Algorithms (Extended abstract)
dc.type	Conference Proceeding
utslib.citation.volume	2023-April
utslib.location.activity	Anaheim, CA, USA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-11-13T03:42:57Z
pubs.finish-date	2023-04-07
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-04-03
pubs.volume	2023-April
dc.location	Piscataway, USA

Abstract:

Data similarity computation is a fundamental research topic which underpins many high level applications based on similarity measures However the exact similarity computation has become daunting in large scale real world scenarios Currently MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore weighted MinHash is utilized to estimate the generalized Jaccard similarity of weighted sets This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms Also we have developed a Python toolbox for the algorithms and released it in our github

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/173371