Canonical consistent weighted sampling for real-valueweighted min-hash

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

Canonical consistent weighted sampling for real-valueweighted min-hash

Wu, W Li, B Chen, L

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2017, pp. 1287 - 1292
Issue Date:: 2017-01-31

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Field	Value	Language
dc.contributor.author	Wu, W	en_US
dc.contributor.author	Li, B	en_US
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2017-01-31	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2017, pp. 1287 - 1292	en_US
dc.identifier.isbn	9781509054725	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/88065
dc.description.abstract	© 2016 IEEE. Min-Hash, as a member of the Locality Sensitive Hashing (LSH) family for sketching sets, plays an important role in the big data era. It is widely used for efficiently estimating similarities of bag-of-words represented data and has been extended to dealing with multi-sets and real-value weighted sets. Improved ConsistentWeighted Sampling (ICWS) has been recognized as the state-of-The-Art for real-value weighted Min- Hash. However, the algorithmic implementation of ICWS is flawed because it violates the uniformity of the Min-Hash scheme. In this paper, we propose a Canonical Consistent Weighted Sampling (CCWS) algorithm, which not only retains the same theoretical complexity as ICWS but also strictly complies with the definition of Min-Hash. The experimental results demonstrate that the proposed CCWS algorithm runs faster than the state-of-The-Arts while achieving similar classification performance on a number of real-world text data sets.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140100545
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2016.39	en_US
dc.title	Canonical consistent weighted sampling for real-valueweighted min-hash	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Min-Hash, as a member of the Locality Sensitive Hashing (LSH) family for sketching sets, plays an important role in the big data era. It is widely used for efficiently estimating similarities of bag-of-words represented data and has been extended to dealing with multi-sets and real-value weighted sets. Improved ConsistentWeighted Sampling (ICWS) has been recognized as the state-of-The-Art for real-value weighted Min- Hash. However, the algorithmic implementation of ICWS is flawed because it violates the uniformity of the Min-Hash scheme. In this paper, we propose a Canonical Consistent Weighted Sampling (CCWS) algorithm, which not only retains the same theoretical complexity as ICWS but also strictly complies with the definition of Min-Hash. The experimental results demonstrate that the proposed CCWS algorithm runs faster than the state-of-The-Arts while achieving similar classification performance on a number of real-world text data sets.

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http://hdl.handle.net/10453/88065