A learning scheme to Fuzzy C-Means based on a compromise in updating membership degrees

Wu, SL; Lin, YY; Liu, YT; Chen, CY; Lin, CT

A learning scheme to Fuzzy C-Means based on a compromise in updating membership degrees

Wu, SL Lin, YY Liu, YT Chen, CY Lin, CT

Permalink

Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2014, pp. 1534 - 1537
Issue Date:: 2014-01-01

Closed Access

	Filename	Description	Size
	06891876.pdf	Published version	2.96 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	Wu, SL	en_US
dc.contributor.author	Lin, YY	en_US
dc.contributor.author	Liu, YT	en_US
dc.contributor.author	Chen, CY	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2014, pp. 1534 - 1537	en_US
dc.identifier.isbn	9781479920723	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119828
dc.description.abstract	© 2014 IEEE. Fuzzy C-Means (FCM) clustering is the most well-known clustering method according to fuzzy partition for pattern classification. However, there are some disadvantages of using that clustering method, such as computational complexity and execution time. Therefore, to solve these drawbacks of FCM, the two-phase FCM procedure has been proposed in this study. Compared with the conventional FCM, the usage of a compromised learning scheme makes more adaptive and effective. By performing the proposed approach, the unknown data could be rapidly clustered according to the previous information. A synthetic data set with two dimensional variables is generated to estimate the performance of the proposed method, and to further demonstrate that our method not only reduces computational complexity but economizes execution time compared with the conventional FCM in each example.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2014.6891773	en_US
dc.title	A learning scheme to Fuzzy C-Means based on a compromise in updating membership degrees	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/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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Fuzzy C-Means (FCM) clustering is the most well-known clustering method according to fuzzy partition for pattern classification. However, there are some disadvantages of using that clustering method, such as computational complexity and execution time. Therefore, to solve these drawbacks of FCM, the two-phase FCM procedure has been proposed in this study. Compared with the conventional FCM, the usage of a compromised learning scheme makes more adaptive and effective. By performing the proposed approach, the unknown data could be rapidly clustered according to the previous information. A synthetic data set with two dimensional variables is generated to estimate the performance of the proposed method, and to further demonstrate that our method not only reduces computational complexity but economizes execution time compared with the conventional FCM in each example.

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

http://hdl.handle.net/10453/119828