A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm
This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings...
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| Veröffentlicht in: | Mathematical problems in engineering 2019, Vol.2019 (2019), p.1-17 |
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| container_title | Mathematical problems in engineering |
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| creator | Zhang, Wenyuan Chen, Jun Huang, Tianyu |
| description | This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images. |
| doi_str_mv | 10.1155/2019/5984649 |
| format | Article |
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This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2019/5984649</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Bias ; Clustering ; Engineering ; Fuzzy sets ; Image classification ; Noise reduction ; Robustness (mathematics) ; Spatial data</subject><ispartof>Mathematical problems in engineering, 2019, Vol.2019 (2019), p.1-17</ispartof><rights>Copyright © 2019 Wenyuan Zhang et al.</rights><rights>Copyright © 2019 Wenyuan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-dd70735ddfb962e3f7815976f10c8d78267853ac721bfea8b1809002d87c60e03</citedby><cites>FETCH-LOGICAL-c360t-dd70735ddfb962e3f7815976f10c8d78267853ac721bfea8b1809002d87c60e03</cites><orcidid>0000-0001-7495-4489</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids></links><search><contributor>Llamazares, Bonifacio</contributor><contributor>Bonifacio Llamazares</contributor><creatorcontrib>Zhang, Wenyuan</creatorcontrib><creatorcontrib>Chen, Jun</creatorcontrib><creatorcontrib>Huang, Tianyu</creatorcontrib><title>A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm</title><title>Mathematical problems in engineering</title><description>This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. 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Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images.</description><subject>Algorithms</subject><subject>Bias</subject><subject>Clustering</subject><subject>Engineering</subject><subject>Fuzzy sets</subject><subject>Image classification</subject><subject>Noise reduction</subject><subject>Robustness (mathematics)</subject><subject>Spatial data</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0M9LwzAUB_AgCs7pzbMUPGpcXtL86HGWTYXJQBS9lbRJt4ytnUmLbH-9HR149PTe4fN-8EXoGsgDAOcjSiAZ8UTFIk5O0AC4YJhDLE-7ntAYA2Vf5-gihBUhFDioAZqNo7c6b0MTPTodcFp7b4vG1VU0bff7XfRp3WLZWBOleO6N9dbgV6urEKXrbsh6Vy2i8XpRe9csN5forNTrYK-OdYg-ppP39BnP5k8v6XiGCyZIg42RRDJuTJknglpWSgU8kaIEUigjFRVScaYLSSEvrVY5KJJ0HxslC0EsYUN02-_d-vq7taHJVnXrq-5kRiknPGZE0U7d96rwdQjeltnWu432uwxIdsgrO-SVHfPq-F3Pl64y-sf9p296bTtjS_2nIREMGPsFGP1yiQ</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Zhang, Wenyuan</creator><creator>Chen, Jun</creator><creator>Huang, Tianyu</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0001-7495-4489</orcidid></search><sort><creationdate>2019</creationdate><title>A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm</title><author>Zhang, Wenyuan ; Chen, Jun ; Huang, Tianyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-dd70735ddfb962e3f7815976f10c8d78267853ac721bfea8b1809002d87c60e03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Bias</topic><topic>Clustering</topic><topic>Engineering</topic><topic>Fuzzy sets</topic><topic>Image classification</topic><topic>Noise reduction</topic><topic>Robustness (mathematics)</topic><topic>Spatial data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Wenyuan</creatorcontrib><creatorcontrib>Chen, Jun</creatorcontrib><creatorcontrib>Huang, Tianyu</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Wenyuan</au><au>Chen, Jun</au><au>Huang, Tianyu</au><au>Llamazares, Bonifacio</au><au>Bonifacio Llamazares</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2019</date><risdate>2019</risdate><volume>2019</volume><issue>2019</issue><spage>1</spage><epage>17</epage><pages>1-17</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2019/5984649</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0001-7495-4489</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Bias Clustering Engineering Fuzzy sets Image classification Noise reduction Robustness (mathematics) Spatial data |
| title | A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm |
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