Method for increasing the computation speed of an unsupervised learning approach for data clustering

Yuwono, M; Su, SW; Moulton, B; Nguyen, H

Method for increasing the computation speed of an unsupervised learning approach for data clustering

Yuwono, M Su, SW

Moulton, B Nguyen, H

Permalink

Publication Type:: Conference Proceeding
Citation:: 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012
Issue Date:: 2012-10-04

Closed Access

	Filename	Description	Size
	2012004160OK.pdf		1.23 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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, B	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2012-10-04	en_US
dc.identifier.citation	2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012	en_US
dc.identifier.isbn	9781467315098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22936
dc.description.abstract	Clustering can be especially effective where the data is irregular, noisy and/or not differentiable. A major obstacle for many clustering techniques is that they are computationally expensive, hence limited to smaller data volume and dimension. We propose a lightweight swarm clustering solution called Rapid Centroid Estimation (RCE). Based on our experiments, RCE has significantly quickened optimization time of its predecessors, Particle Swarm Clustering (PSC) and Modified Particle Swarm Clustering (mPSC). Our experimental results show that on benchmark datasets, RCE produces generally better clusters compared to PSC, mPSC, K-means and Fuzzy C-means. Compared with K-means and Fuzzy C-means which produces clusters with 62% and 55% purities on average respectively, thyroid dataset has successfully clustered on average 71% purity in 14.3 seconds. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 IEEE Congress on Evolutionary Computation, CEC 2012	en_US
dc.relation.isbasedon	10.1109/CEC.2012.6252927	en_US
dc.title	Method for increasing the computation speed of an unsupervised learning approach for data clustering	en_US
dc.type	Conference Proceeding
utslib.for	0802 Computation Theory and Mathematics	en_US
dc.location.activity	Brisbane	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.publication-status	Published	en_US

Abstract:

Clustering can be especially effective where the data is irregular, noisy and/or not differentiable. A major obstacle for many clustering techniques is that they are computationally expensive, hence limited to smaller data volume and dimension. We propose a lightweight swarm clustering solution called Rapid Centroid Estimation (RCE). Based on our experiments, RCE has significantly quickened optimization time of its predecessors, Particle Swarm Clustering (PSC) and Modified Particle Swarm Clustering (mPSC). Our experimental results show that on benchmark datasets, RCE produces generally better clusters compared to PSC, mPSC, K-means and Fuzzy C-means. Compared with K-means and Fuzzy C-means which produces clusters with 62% and 55% purities on average respectively, thyroid dataset has successfully clustered on average 71% purity in 14.3 seconds. © 2012 IEEE.

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

http://hdl.handle.net/10453/22936