A Novel Evolutionary Kernel Intuitionistic Fuzzy C -means Clustering Algorithm
This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel-based fuzzy c-means (KFCM), and genetic algorithms (GA) are optimally used simultaneously to select the parameters of...
Saved in:
| Published in | IEEE transactions on fuzzy systems Vol. 22; no. 5; pp. 1074 - 1087 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.10.2014
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1063-6706 1941-0034 |
| DOI | 10.1109/TFUZZ.2013.2280141 |
Cover
| Abstract | This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel-based fuzzy c-means (KFCM), and genetic algorithms (GA) are optimally used simultaneously to select the parameters of the EKIFCM. The EKIFCM can obtain the advantages of intuitionistic fuzzy sets, kernel functions, and GA in actual clustering problems. Experiments on 2-D synthetic datasets and machine learning repository (http://archive.ics.uci.edu/beta/) datasets show that the proposed EKIFCM is more efficient than conventional algorithms such as the k-means (KM), FCM, Gustafson-Kessel (GK) clustering algorithm, Gath-Geva (GG) clustering algorithm, Chaira's intuitionistic fuzzy c-means (IFCM), and kernel-based fuzzy c-means with Gaussian kernel functions [KFCM(G)] in standard measurement indexes. |
|---|---|
| AbstractList | This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel-based fuzzy c-means (KFCM), and genetic algorithms (GA) are optimally used simultaneously to select the parameters of the EKIFCM. The EKIFCM can obtain the advantages of intuitionistic fuzzy sets, kernel functions, and GA in actual clustering problems. Experiments on 2-D synthetic datasets and machine learning repository (http://archive.ics.uci.edu/beta/) datasets show that the proposed EKIFCM is more efficient than conventional algorithms such as the k-means (KM), FCM, Gustafson-Kessel (GK) clustering algorithm, Gath-Geva (GG) clustering algorithm, Chaira's intuitionistic fuzzy c-means (IFCM), and kernel-based fuzzy c-means with Gaussian kernel functions [KFCM(G)] in standard measurement indexes. |
| Author | Kuo-Ping Lin |
| Author_xml | – sequence: 1 givenname: Kuo-Ping surname: Lin fullname: Lin, Kuo-Ping |
| BookMark | eNp9kD1PwzAURS1UJNrCH4DFI0uKv-I4Y1W1UFGVpV26WI7jFKN8FNtBan89Ca0YGJje09U9T3pnBAZ1UxsA7jGaYIzSp81iu9tNCMJ0QohAmOErMMQpwxFClA26HXEa8QTxGzDy_gN1lRiLIVhP4br5MiWcfzVlG2xTK3eEr8bVXbasQ2v7zPpgNVy0p9MRzmBUGVV7OCtbH4yz9R5Oy33jbHivbsF1oUpv7i5zDLaL-Wb2Eq3enpez6SrSFKEQZcqQXAuRc841p7ESNNMM40xplGcq1bEoDNEqyRXPNUEFZ3lKKc9QmhvFMjoGj-e7B9d8tsYHWVmvTVmq2jStl5iTlHLeqemq4lzVrvHemUJqG1T_VXDKlhIj2SuUPwplr1BeFHYo-YMenK06Qf9DD2fIGmN-AR6LJGGMfgPc6oDJ |
| CODEN | IEFSEV |
| CitedBy_id | crossref_primary_10_1007_s40747_021_00319_8 crossref_primary_10_1002_srin_202400896 crossref_primary_10_1109_TBME_2016_2624306 crossref_primary_10_1007_s40747_024_01459_3 crossref_primary_10_1007_s41066_021_00259_1 crossref_primary_10_1109_TCYB_2019_2925130 crossref_primary_10_1007_s00034_022_02175_4 crossref_primary_10_1016_j_procs_2017_12_100 crossref_primary_10_1038_srep35760 crossref_primary_10_1002_stc_3071 crossref_primary_10_1109_TFUZZ_2020_2966167 crossref_primary_10_1016_j_asoc_2018_02_039 crossref_primary_10_1016_j_eswa_2023_121554 crossref_primary_10_3389_fnbot_2022_715440 crossref_primary_10_1007_s11277_017_5203_2 crossref_primary_10_1109_ACCESS_2017_2715861 crossref_primary_10_1016_j_jngse_2021_104135 crossref_primary_10_1007_s10278_023_00899_6 crossref_primary_10_1109_JBHI_2018_2884208 crossref_primary_10_1109_TIE_2023_3239864 crossref_primary_10_1109_TFUZZ_2015_2501408 crossref_primary_10_1109_TFUZZ_2020_2985930 crossref_primary_10_1007_s11042_023_14512_z crossref_primary_10_3390_app12157385 crossref_primary_10_1016_j_asoc_2017_07_026 crossref_primary_10_1007_s00500_019_04169_y crossref_primary_10_1007_s10032_020_00352_2 crossref_primary_10_1016_j_optlastec_2025_112475 crossref_primary_10_1109_TFUZZ_2018_2852289 crossref_primary_10_1016_j_neucom_2018_11_016 crossref_primary_10_3390_a8020128 crossref_primary_10_1007_s00500_020_04879_8 crossref_primary_10_1016_j_neucom_2018_05_116 crossref_primary_10_3390_rs14153713 crossref_primary_10_1109_ACCESS_2018_2889185 crossref_primary_10_1016_j_ins_2017_03_001 crossref_primary_10_3390_ijerph13090896 crossref_primary_10_1007_s13042_020_01206_3 crossref_primary_10_1007_s41066_023_00446_2 crossref_primary_10_1016_j_neucom_2017_01_017 crossref_primary_10_3390_sym12040562 crossref_primary_10_1007_s10462_022_10236_y crossref_primary_10_1007_s00521_016_2292_x crossref_primary_10_1016_j_eswa_2016_07_040 crossref_primary_10_1007_s40815_023_01644_5 crossref_primary_10_1109_TCYB_2016_2634599 crossref_primary_10_1109_TCYB_2019_2902603 crossref_primary_10_1007_s43684_023_00055_5 crossref_primary_10_1109_JBHI_2018_2803020 crossref_primary_10_1016_j_future_2017_06_010 crossref_primary_10_1016_j_eswa_2015_04_008 crossref_primary_10_1109_TIM_2022_3196702 crossref_primary_10_1016_j_eswa_2019_113102 crossref_primary_10_1109_ACCESS_2024_3462443 crossref_primary_10_1080_1206212X_2019_1662984 crossref_primary_10_1109_TFUZZ_2016_2612300 crossref_primary_10_3233_JIFS_212647 crossref_primary_10_1080_21681163_2022_2156927 crossref_primary_10_1515_jisys_2016_0241 crossref_primary_10_1109_TFUZZ_2016_2639565 crossref_primary_10_1016_j_engappai_2019_05_004 crossref_primary_10_3390_electronics9010046 crossref_primary_10_1016_j_neucom_2019_01_042 crossref_primary_10_1007_s41060_023_00474_w crossref_primary_10_1109_TFUZZ_2020_3029296 crossref_primary_10_1016_j_bspc_2021_103260 crossref_primary_10_1109_TITS_2018_2875159 crossref_primary_10_1007_s42979_021_00722_5 crossref_primary_10_1109_TFUZZ_2019_2956900 crossref_primary_10_2139_ssrn_4158293 crossref_primary_10_1109_ACCESS_2019_2963444 crossref_primary_10_1109_TFUZZ_2016_2637373 crossref_primary_10_3233_JIFS_211093 crossref_primary_10_1016_j_asoc_2021_107755 crossref_primary_10_3390_rs14051117 crossref_primary_10_1109_TFUZZ_2018_2809691 crossref_primary_10_1016_j_cmpb_2015_08_001 crossref_primary_10_1016_j_asoc_2019_105838 crossref_primary_10_1109_JAS_2020_1003420 crossref_primary_10_1109_JSEN_2018_2813984 crossref_primary_10_1109_ACCESS_2018_2809456 crossref_primary_10_1088_1361_6501_adbb0a crossref_primary_10_1155_2019_5092147 crossref_primary_10_1007_s13042_016_0614_z crossref_primary_10_1109_ACCESS_2024_3512416 crossref_primary_10_1109_ACCESS_2020_2968936 crossref_primary_10_1109_TFUZZ_2017_2756827 crossref_primary_10_5004_dwt_2019_23360 crossref_primary_10_1007_s00500_015_1712_7 crossref_primary_10_3390_sym9110266 crossref_primary_10_1016_j_asoc_2023_111196 crossref_primary_10_3390_sym14071442 crossref_primary_10_3923_jse_2017_172_182 crossref_primary_10_1109_TCYB_2019_2909037 crossref_primary_10_1016_j_asoc_2024_112639 crossref_primary_10_1016_j_eswa_2017_07_048 crossref_primary_10_1016_j_asoc_2017_05_025 crossref_primary_10_1109_TCYB_2018_2861211 crossref_primary_10_1007_s10489_016_0759_1 crossref_primary_10_5391_IJFIS_2013_13_4_254 crossref_primary_10_1109_TCYB_2022_3217897 crossref_primary_10_1109_TSMC_2017_2756447 crossref_primary_10_1007_s00371_021_02319_8 crossref_primary_10_1007_s00500_023_09533_7 crossref_primary_10_1109_TCYB_2021_3099503 crossref_primary_10_1007_s00500_023_09367_3 crossref_primary_10_1080_18756891_2016_1175814 crossref_primary_10_3390_e19110578 crossref_primary_10_1007_s11063_018_9881_x crossref_primary_10_1109_TCYB_2019_2921779 crossref_primary_10_1109_TFUZZ_2019_2917809 crossref_primary_10_1007_s42835_022_01074_7 crossref_primary_10_1142_S0219622021500607 |
| Cites_doi | 10.1023/B:NEPL.0000011135.19145.1b 10.1016/j.artmed.2004.01.012 10.1109/TFUZZ.2011.2174366 10.1016/j.asoc.2010.05.005 10.1016/j.fss.2008.03.018 10.1016/j.patrec.2008.05.019 10.1016/0165-0114(95)00154-9 10.1109/34.192473 10.1016/j.patcog.2011.02.009 10.1109/JSTSP.2010.2096797 10.1016/j.fss.2010.07.005 10.1109/TFUZZ.2012.2187062 10.1109/TFUZZ.2012.2187453 10.1109/TSMCB.2011.2124455 10.1109/TSMCB.2002.1033180 10.1023/A:1007608224229 10.1093/bioinformatics/btl560 10.1016/0167-8655(93)90058-L 10.1109/T-C.1969.222678 10.1007/s00500-005-0043-5 10.1016/S0167-8655(99)00069-0 10.1109/TFUZZ.2012.2201485 10.1016/j.eswa.2011.11.063 10.1007/978-3-540-88458-3_69 10.1007/978-3-642-97966-8 10.1109/TFUZZ.2011.2179303 10.1109/TFUZZ.2011.2182354 10.1111/j.1469-1809.1936.tb02137.x 10.1109/TFUZZ.2011.2170175 10.3969/j.issn.1004-4132.2010.04.009 10.1016/j.amc.2010.11.055 10.1109/TFUZZ.2011.2179659 10.1109/TFUZZ.2012.2226942 10.1109/TFUZZ.2011.2175400 10.1109/TFUZZ.2012.2215331 10.1007/978-3-642-15660-1_45 10.1080/01969727308546046 10.1016/S0165-0114(98)00402-3 10.1016/0165-0114(89)90215-7 10.1007/978-1-4757-0450-1 10.1118/1.597000 10.1109/5326.897072 10.1108/03684929710176502 10.1016/j.fss.2009.10.021 10.1109/TFUZZ.2011.2173693 10.1016/j.fss.2009.10.019 10.1109/91.493905 10.1109/TFUZZ.2006.889763 10.1016/j.patcog.2009.04.013 10.1016/j.fss.2007.12.030 10.1016/j.media.2008.06.014 10.1109/TSMCB.2008.2004818 10.1109/TFUZZ.2013.2255613 10.1109/TFUZZ.2004.825073 10.1109/TSMCC.2008.2007252 10.1016/j.eswa.2012.02.167 10.1007/978-3-7908-1870-3 10.1016/S0019-9958(65)90241-X 10.1016/S0165-0114(86)80034-3 10.1109/34.85677 10.1016/0895-7177(93)90202-A 10.1098/rsta.1909.0016 10.1016/j.fss.2009.06.015 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD F28 FR3 JQ2 L7M L~C L~D |
| DOI | 10.1109/TFUZZ.2013.2280141 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1941-0034 |
| EndPage | 1087 |
| ExternalDocumentID | 10_1109_TFUZZ_2013_2280141 6587744 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Science Council of the Republic of China grantid: NSC 102-2410-H-262 -008 |
| GroupedDBID | -~X .DC 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS TAE TN5 VH1 AAYXX CITATION 7SC 7SP 8FD F28 FR3 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c300t-bae2dc88d666c635a83bc411bac0dba9c58fe2ca7da6dc20f64d9336b09dea4b3 |
| IEDL.DBID | RIE |
| ISSN | 1063-6706 |
| IngestDate | Thu Oct 02 05:07:26 EDT 2025 Thu Apr 24 23:06:24 EDT 2025 Wed Oct 01 02:37:20 EDT 2025 Tue Aug 26 16:50:06 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c300t-bae2dc88d666c635a83bc411bac0dba9c58fe2ca7da6dc20f64d9336b09dea4b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1629366109 |
| PQPubID | 23500 |
| PageCount | 14 |
| ParticipantIDs | proquest_miscellaneous_1629366109 crossref_citationtrail_10_1109_TFUZZ_2013_2280141 crossref_primary_10_1109_TFUZZ_2013_2280141 ieee_primary_6587744 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2014-Oct. 2014-10-00 20141001 |
| PublicationDateYYYYMMDD | 2014-10-01 |
| PublicationDate_xml | – month: 10 year: 2014 text: 2014-Oct. |
| PublicationDecade | 2010 |
| PublicationTitle | IEEE transactions on fuzzy systems |
| PublicationTitleAbbrev | TFUZZ |
| PublicationYear | 2014 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref12 (ref68) 0 ref59 ref15 ref58 ref14 ref53 ref52 ref11 ref54 ref10 ref17 ref16 szmidt (ref48) 2006; 4029 ref19 ref18 gustafson (ref57) 1979 chaira (ref37) 2011; 11 (ref64) 0 ref46 ref45 vapnik (ref51) 1998 (ref65) 0 ref47 ref42 ref41 ref44 mangasarian (ref70) 1990; 23 ref43 ref49 ref7 ref9 ref3 ref6 ref5 ref40 ref34 ref36 haberman (ref66) 1976 ref31 ref74 ref30 ref33 ref32 ref2 ref1 ref39 ref38 holland (ref55) 1975 kohonen (ref4) 1997 ref71 ref73 ref72 macqueen (ref56) 1967; 1 iakovidis (ref35) 2008; 5259 ref24 ref67 ref23 ref69 ref25 ref20 ref63 ref22 ref21 ref28 ref27 ref29 zhu (ref26) 2009; 39 atanassov (ref50) 1999 ref60 ref62 ref61 jain (ref8) 1988 |
| References_xml | – ident: ref39 doi: 10.1023/B:NEPL.0000011135.19145.1b – ident: ref40 doi: 10.1016/j.artmed.2004.01.012 – ident: ref16 doi: 10.1109/TFUZZ.2011.2174366 – volume: 11 start-page: 1711 year: 2011 ident: ref37 article-title: A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2010.05.005 – ident: ref41 doi: 10.1016/j.fss.2008.03.018 – ident: ref42 doi: 10.1016/j.patrec.2008.05.019 – ident: ref46 doi: 10.1016/0165-0114(95)00154-9 – ident: ref60 doi: 10.1109/34.192473 – ident: ref44 doi: 10.1016/j.patcog.2011.02.009 – ident: ref32 doi: 10.1109/JSTSP.2010.2096797 – ident: ref58 doi: 10.1016/j.fss.2010.07.005 – ident: ref21 doi: 10.1109/TFUZZ.2012.2187062 – ident: ref22 doi: 10.1109/TFUZZ.2012.2187453 – year: 1988 ident: ref8 publication-title: Algorithm for Clustering Data – ident: ref6 doi: 10.1109/TSMCB.2011.2124455 – ident: ref59 doi: 10.1109/TSMCB.2002.1033180 – volume: 4029 start-page: 314 year: 2006 ident: ref48 article-title: An application of intuitionistic fuzzy set publication-title: Lect Notes Artif Intell – ident: ref69 doi: 10.1023/A:1007608224229 – ident: ref5 doi: 10.1093/bioinformatics/btl560 – ident: ref74 doi: 10.1016/0167-8655(93)90058-L – ident: ref72 doi: 10.1109/T-C.1969.222678 – start-page: 104 year: 1976 ident: ref66 article-title: Generalized residuals for log-linear models publication-title: Proc Int'l Conf Biometrics – ident: ref54 doi: 10.1007/s00500-005-0043-5 – ident: ref73 doi: 10.1016/S0167-8655(99)00069-0 – ident: ref23 doi: 10.1109/TFUZZ.2012.2201485 – ident: ref33 doi: 10.1016/j.eswa.2011.11.063 – volume: 5259 start-page: 764 year: 2008 ident: ref35 article-title: Intuitionistic fuzzy clustering with applications in computer vision publication-title: Lect Notes Comput Sci doi: 10.1007/978-3-540-88458-3_69 – year: 1997 ident: ref4 publication-title: Self-Organizing Maps doi: 10.1007/978-3-642-97966-8 – ident: ref18 doi: 10.1109/TFUZZ.2011.2179303 – ident: ref20 doi: 10.1109/TFUZZ.2011.2182354 – ident: ref67 doi: 10.1111/j.1469-1809.1936.tb02137.x – ident: ref14 doi: 10.1109/TFUZZ.2011.2170175 – ident: ref36 doi: 10.3969/j.issn.1004-4132.2010.04.009 – ident: ref52 doi: 10.1016/j.amc.2010.11.055 – ident: ref19 doi: 10.1109/TFUZZ.2011.2179659 – ident: ref13 doi: 10.1109/TFUZZ.2012.2226942 – year: 1975 ident: ref55 publication-title: Adaptation in Natural and Artificial System – ident: ref17 doi: 10.1109/TFUZZ.2011.2175400 – ident: ref12 doi: 10.1109/TFUZZ.2012.2215331 – ident: ref30 doi: 10.1007/978-3-642-15660-1_45 – ident: ref63 doi: 10.1080/01969727308546046 – ident: ref47 doi: 10.1016/S0165-0114(98)00402-3 – ident: ref49 doi: 10.1016/0165-0114(89)90215-7 – ident: ref1 doi: 10.1007/978-1-4757-0450-1 – ident: ref24 doi: 10.1118/1.597000 – ident: ref10 doi: 10.1109/5326.897072 – ident: ref71 doi: 10.1108/03684929710176502 – start-page: 761 year: 1979 ident: ref57 article-title: Fuzzy clustering with a Fuzzy covariance matrix publication-title: Proc IEEE Conf Decision Control – ident: ref43 doi: 10.1016/j.fss.2009.10.021 – ident: ref15 doi: 10.1109/TFUZZ.2011.2173693 – ident: ref31 doi: 10.1016/j.fss.2009.10.019 – ident: ref61 doi: 10.1109/91.493905 – year: 0 ident: ref65 – ident: ref9 doi: 10.1109/TFUZZ.2006.889763 – ident: ref27 doi: 10.1016/j.patcog.2009.04.013 – ident: ref25 doi: 10.1016/j.fss.2007.12.030 – year: 0 ident: ref64 – ident: ref29 doi: 10.1016/j.media.2008.06.014 – year: 1998 ident: ref51 publication-title: Statistical Learning Theory – volume: 39 start-page: 578 year: 2009 ident: ref26 article-title: Generalized fuzzy c-means clustering algorithm with improve fuzzy parttions publication-title: IEEE Trans Syst Man Cybern B Cybern doi: 10.1109/TSMCB.2008.2004818 – ident: ref11 doi: 10.1109/TFUZZ.2013.2255613 – volume: 23 start-page: 1 year: 1990 ident: ref70 article-title: Cancer diagnosis via linear programming publication-title: SIAM News – ident: ref3 doi: 10.1109/TFUZZ.2004.825073 – ident: ref7 doi: 10.1109/TSMCC.2008.2007252 – ident: ref38 doi: 10.1016/j.eswa.2012.02.167 – year: 1999 ident: ref50 publication-title: Intuitionistic Fuzzy Sets Theory and Applications doi: 10.1007/978-3-7908-1870-3 – year: 0 ident: ref68 – ident: ref45 doi: 10.1016/S0019-9958(65)90241-X – ident: ref34 doi: 10.1016/S0165-0114(86)80034-3 – ident: ref62 doi: 10.1109/34.85677 – volume: 1 start-page: 281 year: 1967 ident: ref56 article-title: Some methods for classification and analysis of multivariate observations publication-title: Proc 5th Berkeley Symp Math Statist Probability Univ California Press – ident: ref2 doi: 10.1016/0895-7177(93)90202-A – ident: ref53 doi: 10.1098/rsta.1909.0016 – ident: ref28 doi: 10.1016/j.fss.2009.06.015 |
| SSID | ssj0014518 |
| Score | 2.4810758 |
| Snippet | This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1074 |
| SubjectTerms | Algorithm design and analysis Algorithms Clustering Clustering algorithms Evolutionary Evolutionary kernel intuitionistic fuzzy c -means (EKIFCM) Fuzzy fuzzy c -means (FCM) Fuzzy logic Fuzzy set theory Fuzzy sets genetic algorithm (GA) Genetic algorithms Image segmentation intuitionistic fuzzy sets Kernel kernel function Kernels Linear programming Prototypes |
| Title | A Novel Evolutionary Kernel Intuitionistic Fuzzy C -means Clustering Algorithm |
| URI | https://ieeexplore.ieee.org/document/6587744 https://www.proquest.com/docview/1629366109 |
| Volume | 22 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0034 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014518 issn: 1063-6706 databaseCode: RIE dateStart: 19930101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Nb9QwEB21PcGBlhbEQouMxA28tWOvYx9Xq67aIvbUlapeIn8FULcJWpJK3V9f20lWfAlxiyI7sfTGmefMzBuA90oqQ7KyxCTPLebCEqy8VdiwzCgnWOCoSe1zIc6X_PJ6cr0DH7e1MN77lHzmx_EyxfJdbdv4q-w0eMvAVvgu7OZSdLVa24gBn9Cu7E0wLHIihgIZok6v5subm5jFxcZR_IVy-osTSl1V_vgUJ_8y34fPw8q6tJLbcduYsd38Jtr4v0s_gGc90UTTzjKew46vDmF_aOKA-j19CE9_UiQ8gsUULep7v0Jn971N6vUD-uTXVbh3EfxTyvBK4s5o3m42D2iG8J0P_g7NVm0UXQiPQdPVl3r9rfl69wKW87Or2TnuWy5gywhpsNE-c1ZKF041NnARLZmxnFKjLXFGKzuRpc-szp0WzmakFNwpxoQhynnNDXsJe1Vd-VeAWBjMS6qpY4GlSKkkV6UynhHLJp64EdABg8L2euSxLcaqSOcSooqEWxFxK3rcRvBhO-d7p8bxz9FHEYjtyB6DEbwboC7CXooBEl35uv1RUBHIj4gC9K__PvUNPAkv4F0y3zHsNevWnwRS0pi3yRofAWgx3mo |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3Pb9MwFH4a4wAcGGxMFBgYiRu4s2PHjY9VtapjW0-tNO0S-VdgoktQSSatfz22k1T8mBC3KLITS99z3ue8974H8EFmUpOkKDAZjQzmwhAsnZFYs0RLK5jnqFHtcy5mS_75Mr3cgU_bWhjnXEw-c8NwGWP5tjJN-FV27L2lZyv8ATxMOedpW621jRnwlLaFb4JhMSKiL5Eh8ngxXV5dhTwuNgzyL5TT39xQ7Kvy18c4epjpHlz0a2sTS74Nm1oPzeYP2cb_XfwzeNpRTTRubeM57LhyH_b6Ng6o29X78OQXTcIDmI_RvLp1K3Ry21mlWt-hM7cu_b1T76FijleUd0bTZrO5QxOEb5z3eGiyaoLsgn8MGq--VOvr-uvNC1hOTxaTGe6aLmDDCKmxVi6xJsusP9cYz0ZUxrThlGpliNVKmjQrXGLUyCphTUIKwa1kTGgirVNcs0PYLavSvQTE_GBeUEUt8zwly2TGZSG1Y8Sw1BE7ANpjkJtOkTw0xljl8WRCZB5xywNueYfbAD5u53xv9Tj-OfogALEd2WEwgPc91LnfTSFEokpXNT9yKjz9EUGC_tX9U9_Bo9ni4jw_P52fvYbH_mW8Te17A7v1unFHnqLU-m20zJ80KOG3 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Novel+Evolutionary+Kernel+Intuitionistic+Fuzzy+C+-means+Clustering+Algorithm&rft.jtitle=IEEE+transactions+on+fuzzy+systems&rft.au=Lin%2C+Kuo-Ping&rft.date=2014-10-01&rft.issn=1063-6706&rft.eissn=1941-0034&rft.volume=22&rft.issue=5&rft.spage=1074&rft.epage=1087&rft_id=info:doi/10.1109%2FTFUZZ.2013.2280141&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1063-6706&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1063-6706&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1063-6706&client=summon |