An algorithm for scalable clustering: Ensemble Rapid Centroid Estimation

Yuwono, M; Su, S; Moulton, BD; Guo, Y; Nguyen, HT

An algorithm for scalable clustering: Ensemble Rapid Centroid Estimation

Yuwono, M Su, S

Moulton, BD Guo, Y Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, 2014, pp. 1250 - 1257
Issue Date:: 2014-09-16

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Field	Value	Language
dc.contributor.author	Yuwono, M	en_US
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Moulton, BD	en_US
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2014-09-16	en_US
dc.identifier.citation	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, 2014, pp. 1250 - 1257	en_US
dc.identifier.isbn	9781479914883	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35577
dc.description.abstract	© 2014 IEEE. This paper describes a new algorithm, called Ensemble Rapid Centroid Estimation (ERCE), designed to handle large-scale non-convex cluster optimization tasks, and estimate the number of clusters with quasi-linear complexity. ERCE stems from a recently developed Rapid Centroid Estimation (RCE) algorithm. RCE was originally developed as a lightweight simplification of the Particle Swarm Clustering (PSC) algorithm. RCE retained the quality of PSC, greatly reduced the computational complexity, and increased the stability. However, RCE has certain limitations with respect to complexity, and is unsuitable for non-convex clusters. The new ERCE algorithm presented here addresses these limitations.	en_US
dc.relation.ispartof	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014	en_US
dc.relation.isbasedon	10.1109/CEC.2014.6900295	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	An algorithm for scalable clustering: Ensemble Rapid Centroid Estimation	en_US
dc.type	Conference Proceeding
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
dc.location.activity	Beijing
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.publication-status	Published	en_US

Abstract:

© 2014 IEEE. This paper describes a new algorithm, called Ensemble Rapid Centroid Estimation (ERCE), designed to handle large-scale non-convex cluster optimization tasks, and estimate the number of clusters with quasi-linear complexity. ERCE stems from a recently developed Rapid Centroid Estimation (RCE) algorithm. RCE was originally developed as a lightweight simplification of the Particle Swarm Clustering (PSC) algorithm. RCE retained the quality of PSC, greatly reduced the computational complexity, and increased the stability. However, RCE has certain limitations with respect to complexity, and is unsuitable for non-convex clusters. The new ERCE algorithm presented here addresses these limitations.

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http://hdl.handle.net/10453/35577