Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/79743
DC Field | Value | Language |
---|---|---|
dc.contributor | Institute of Textiles and Clothing | - |
dc.creator | Zhou, J | - |
dc.creator | Lai, ZH | - |
dc.creator | Gao, C | - |
dc.creator | Yue, XD | - |
dc.creator | Wong, WK | - |
dc.date.accessioned | 2018-12-21T07:13:15Z | - |
dc.date.available | 2018-12-21T07:13:15Z | - |
dc.identifier.issn | 2169-3536 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/79743 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. | en_US |
dc.rights | Posted with permission of the publisher. | en_US |
dc.rights | The following publication Zhou, J., Lai, Z. H., Gao, C., Yue, X. D., & Wong, W. K.(2018). Rough-fuzzy clustering basedon two-stage three-way approximations. IEEE Access, 6, 27541-27554 is available at https://dx.doi.org/10.1109/ACCESS.2018.2834348 | en_US |
dc.subject | Rough sets | en_US |
dc.subject | Rough-fuzzy clustering | en_US |
dc.subject | Three-way approximations | en_US |
dc.subject | Fuzziness | en_US |
dc.subject | Shadowed sets | en_US |
dc.title | Rough-fuzzy clustering basedon two-stage three-way approximations | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 27541 | en_US |
dc.identifier.epage | 27554 | en_US |
dc.identifier.volume | 6 | en_US |
dc.identifier.doi | 10.1109/ACCESS.2018.2834348 | en_US |
dcterms.abstract | A general framework of rough-fuzzy clustering based on two-stage three-way approximations is presented in this paper. The proposed framework can deal with the uncertainties caused by the membership degree distributions of patterns. In the first stage (macro aspect), three-way approximations with respect to a fixed cluster can be formed from the global observation on data which can capture the data topology well about this cluster. In the second stage (micro aspect), the fuzziness of individual patterns over all clusters can be measured with De Luca and Termini's method, based on which three-way approximations with respect to the whole data set can be generated such that the uncertainties of the locations of individual patterns can be detected. By integrating the approximation region partitions obtained in the two stages, i.e., using the partition results obtained in the second stage to modify the partition results obtained in the first stage, the misled prototype calculations can be verified and the obtained prototypes tend to their natural positions. Comparative experiments on a synthetic data set and some benchmark data sets demonstrate the improved performance of the proposed method. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2018, v. 6, p. 27541-27554 | - |
dcterms.isPartOf | IEEE access | - |
dcterms.issued | 2018 | - |
dc.identifier.isi | WOS:000434694000001 | - |
dc.identifier.rosgroupid | 2017006877 | - |
dc.description.ros | 2017-2018 > Academic research: refereed > Publication in refereed journal | - |
dc.description.validate | 201812 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Publisher permission | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhou_Rough-fuzzy_Clustering_Basedon.pdf | 7.08 MB | Adobe PDF | View/Open |
Page views
77
Last Week
0
0
Last month
Citations as of Sep 8, 2024
Downloads
59
Citations as of Sep 8, 2024
SCOPUSTM
Citations
10
Citations as of Jun 21, 2024
WEB OF SCIENCETM
Citations
10
Last Week
0
0
Last month
Citations as of Sep 19, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.