Data clustering using variants of rapid centroid estimation

Yuwono, M; Su, SW; Moulton, BD; Nguyen, HT

Data clustering using variants of rapid centroid estimation

Yuwono, M Su, SW

Moulton, BD Nguyen, HT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Evolutionary Computation, 2014, 18 (3), pp. 366 - 377
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Yuwono, M	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Moulton, BD	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Transactions on Evolutionary Computation, 2014, 18 (3), pp. 366 - 377	en_US
dc.identifier.issn	1089-778X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33371
dc.description.abstract	Prior work suggests that particle swarm clustering (PSC) can be a powerful tool for solving clustering problems. This paper reviews parts of the PSC algorithm, and shows how and why a new class of algorithms is proposed in an attempt to improve the efficiency and repeatability of PSC. This new implementation is referred to as rapid centroid estimation (RCE). RCE simplifies the update rules of PSC, and greatly reduces computational complexity by enhancing the efficiency of the particle trajectories. On benchmark evaluations with an artificial dataset that has 80 dimensions and a volume of 5000, the RCE variants have iteration times of less than 0.1 s, which compares to iteration times of 2 s for PSC and modified PSC (mPSC). On UC Irvine (UCI) machine learning benchmark datasets, the RCE variants are much faster than PSC and mPSC, and produce clusters with higher purity and greatly improved optimization speeds. For example, the RCE variants are more than 100 times faster than PSC and mPSC on the UCI breast cancer dataset. It can be concluded that the RCE variants are leaner and faster than PSC and mPSC, and that the new optimization strategies also improve clustering quality and repeatability. © 2013 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Evolutionary Computation	en_US
dc.relation.isbasedon	10.1109/TEVC.2013.2281545	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Data clustering using variants of rapid centroid estimation	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	18	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	18	en_US

Abstract:

Prior work suggests that particle swarm clustering (PSC) can be a powerful tool for solving clustering problems. This paper reviews parts of the PSC algorithm, and shows how and why a new class of algorithms is proposed in an attempt to improve the efficiency and repeatability of PSC. This new implementation is referred to as rapid centroid estimation (RCE). RCE simplifies the update rules of PSC, and greatly reduces computational complexity by enhancing the efficiency of the particle trajectories. On benchmark evaluations with an artificial dataset that has 80 dimensions and a volume of 5000, the RCE variants have iteration times of less than 0.1 s, which compares to iteration times of 2 s for PSC and modified PSC (mPSC). On UC Irvine (UCI) machine learning benchmark datasets, the RCE variants are much faster than PSC and mPSC, and produce clusters with higher purity and greatly improved optimization speeds. For example, the RCE variants are more than 100 times faster than PSC and mPSC on the UCI breast cancer dataset. It can be concluded that the RCE variants are leaner and faster than PSC and mPSC, and that the new optimization strategies also improve clustering quality and repeatability. © 2013 IEEE.

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http://hdl.handle.net/10453/33371