Fuzziness parameter selection in fuzzy c-means： The perspective of cluster validation
Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index （CVI） is a kind of criterion function to validate the clusterin...
Saved in:
| Published in: | Science China. Information sciences Vol. 57; no. 11; pp. 247 - 254 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Heidelberg
Science China Press
01.11.2014
Springer Nature B.V |
| Subjects: | |
| ISSN: | 1674-733X, 1869-1919 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index （CVI） is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3]. |
|---|---|
| AbstractList | Fuzzy c-means (FCM) algorithm is an important clustering method in pattern recognition, while the fuzziness parameter,
m
, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index (CVI) is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of
m
in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of
m
is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of
m
is [2, 3.5] and the optimal interval is [2.5, 3]. Fuzzy c-means (FCM) algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index (CVI) is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3]. Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index （CVI） is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3]. |
| Author | ZHOU KaiLe FU Chao YANG ShanLin |
| AuthorAffiliation | School of Management, Hefei University of Technology, Hefei 230009, China Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education Hefei 230009, China |
| Author_xml | – sequence: 1 givenname: KaiLe surname: Zhou fullname: Zhou, KaiLe email: kailezhou@gmail.com organization: School of Management, Hefei University of Technology, Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education – sequence: 2 givenname: Chao surname: Fu fullname: Fu, Chao organization: School of Management, Hefei University of Technology, Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education – sequence: 3 givenname: ShanLin surname: Yang fullname: Yang, ShanLin organization: School of Management, Hefei University of Technology, Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education |
| BookMark | eNp9kE9KxDAUh4MoqKMHcBd04yaal7ZJuxRxVBDcKLoLmfZlptJJa9IKzlE8i3fyCqaOKLgwm5fF970_v12y6VqHhBwAPwHO1WkASBPBOKQsg1QyvkF2IJcFgwKKzfiXKmUqSR63yX4ITzy-JOFC5TvkYTqsVrXDEGhnvFlij54GbLDs69bR2lEbgVdasiUaFz7e3-jdAmmHPnQj84K0tbRshjCKL6apKzOae2TLmibg_nedkPvpxd35Fbu5vbw-P7thZaJkz2xeZWmlQFVcZlVlBa-UzWcgZkZWwgiDqCzPRWkQDBc5KqmEKDgoWdhZvGJCjtd9O98-Dxh6vaxDiU1jHLZD0CBjIiBkoSJ69Ad9agfv4nZaFJBnopCZjJRaU6VvQ_BodVn3Xyf13tSNBq7HzPU6cx0z12PmmkcT_pidr5fGv_7riLUTIuvm6H93-k86_B60aN38OXo_k6QUGVciU8knzvKiIQ |
| CitedBy_id | crossref_primary_10_3233_IDA_215857 crossref_primary_10_1080_01605682_2022_2064782 crossref_primary_10_1007_s12145_021_00680_9 crossref_primary_10_1016_j_enbuild_2021_111376 crossref_primary_10_1080_00051144_2023_2244307 crossref_primary_10_1080_17538157_2025_2550968 crossref_primary_10_1109_TFUZZ_2018_2856081 crossref_primary_10_1007_s11432_014_5267_5 crossref_primary_10_1109_ACCESS_2020_2985425 crossref_primary_10_5194_amt_17_1811_2024 crossref_primary_10_1109_TFUZZ_2022_3217343 crossref_primary_10_3389_fgene_2021_667936 crossref_primary_10_1002_smr_2260 crossref_primary_10_1080_10618600_2025_2489537 crossref_primary_10_4054_DemRes_2024_51_16 crossref_primary_10_1109_TNSM_2020_2966951 crossref_primary_10_3390_fi13090233 crossref_primary_10_1109_TFUZZ_2025_3581918 crossref_primary_10_3233_IDA_216058 crossref_primary_10_7717_peerj_5416 crossref_primary_10_3233_IFS_151811 crossref_primary_10_1080_00051144_2023_2284027 |
| Cites_doi | 10.1016/j.ins.2007.06.028 10.1016/j.patrec.2009.09.011 10.3233/IDA-2012-0545 10.1109/TPAMI.2002.1114856 10.1016/0031-3203(92)90102-O 10.1109/72.159057 10.1016/j.patrec.2010.11.006 10.1007/BF02294713 10.1016/j.patrec.2012.08.014 10.1007/978-1-4757-0450-1 10.1049/el:19981523 10.1016/S1361-8415(00)00035-9 10.1109/TSMCB.2003.810951 10.1109/34.85677 10.1109/TFUZZ.2006.889763 10.1016/j.patrec.2005.04.007 10.1109/TSMC.1976.4309506 10.1109/TSMC.1987.6499296 10.1016/0098-3004(84)90020-7 10.1016/j.patrec.2010.08.007 10.1016/j.patcog.2011.07.012 10.1016/S0167-8655(00)00016-7 10.1109/91.413225 10.1016/j.fss.2007.03.004 10.1109/TPAMI.1986.4767778 10.1109/TNN.2005.845141 10.5121/ijcses.2010.1207 10.1016/j.patcog.2012.07.021 10.1007/s11432-012-4748-7 10.1109/TFUZZ.1997.554463 10.1109/FUZZY.1992.258640 10.1109/91.493905 10.1016/j.knosys.2012.05.012 10.1016/j.patcog.2010.04.025 10.1080/01969727308546046 10.1016/j.ins.2007.10.004 |
| ContentType | Journal Article |
| Copyright | Science China Press and Springer-Verlag Berlin Heidelberg 2014 Science China Press and Springer-Verlag Berlin Heidelberg 2014. |
| Copyright_xml | – notice: Science China Press and Springer-Verlag Berlin Heidelberg 2014 – notice: Science China Press and Springer-Verlag Berlin Heidelberg 2014. |
| DBID | 2RA 92L CQIGP W92 ~WA AAYXX CITATION 8FE 8FG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI 7SC 8FD L7M L~C L~D |
| DOI | 10.1007/s11432-014-5146-0 |
| DatabaseName | 维普_期刊 中文科技期刊数据库-CALIS站点 维普中文期刊数据库 中文科技期刊数据库-工程技术 中文科技期刊数据库- 镜像站点 CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College ProQuest Central Korea ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Computer and Information Systems Abstracts Technology Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Advanced Technologies & Aerospace Collection Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Advanced Technologies & Aerospace Collection Computer and Information Systems Abstracts |
| Database_xml | – sequence: 1 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| DocumentTitleAlternate | Fuzziness parameter selection in fuzzy c-means： The perspective of cluster validation |
| EISSN | 1869-1919 |
| EndPage | 254 |
| ExternalDocumentID | 10_1007_s11432_014_5146_0 662507257 |
| GroupedDBID | -59 -5G -BR -EM -Y2 -~C .VR 06D 0VY 1N0 2B. 2C. 2J2 2JN 2JY 2KG 2KM 2LR 2RA 2VQ 2~H 30V 4.4 406 40D 40E 5VR 5VS 8TC 8UJ 92E 92I 92L 92Q 93N 95- 95. 96X AAAVM AABHQ AAFGU AAHNG AAIAL AAJKR AANZL AARHV AARTL AATNV AATVU AAUYE AAWCG AAYFA AAYIU AAYQN AAYTO ABBBX ABDZT ABECU ABFGW ABFTV ABHQN ABJNI ABJOX ABKAS ABKCH ABKTR ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABWNU ABXPI ACAOD ACBMV ACBRV ACBXY ACBYP ACGFO ACGFS ACHSB ACHXU ACIGE ACIPQ ACKNC ACMDZ ACMLO ACOKC ACOMO ACREN ACSNA ACTTH ACVWB ACWMK ACZOJ ADHIR ADINQ ADKNI ADKPE ADMDM ADOXG ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEBTG AEFTE AEGAL AEGNC AEJHL AEJRE AEOHA AEPYU AESKC AESTI AETLH AEVLU AEVTX AEXYK AFKRA AFLOW AFNRJ AFQWF AFUIB AFWTZ AFYQB AFZKB AGAYW AGDGC AGGBP AGJBK AGMZJ AGQMX AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIIXL AILAN AIMYW AITGF AJBLW AJDOV AJRNO AJZVZ AKQUC ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AZFZN B-. BDATZ BENPR BGLVJ BGNMA CAG CCEZO CCPQU CHBEP COF CQIGP CSCUP CUBFJ CW9 DDRTE DNIVK DPUIP EBLON EBS EIOEI EJD ESBYG FA0 FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 HCIFZ HG6 HMJXF HRMNR HVGLF HZ~ IJ- IKXTQ IWAJR IXD I~X I~Z J-C JBSCW JZLTJ K7- KOV LLZTM M4Y MA- N2Q NB0 NPVJJ NQJWS NU0 O9J P9O PF0 PT4 QOS R89 RIG ROL RSV S16 S3B SAP SCL SCO SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN TCJ TGP TR2 TSG TUC U2A UG4 UNUBA UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 W92 WK8 YLTOR Z7R Z7S Z7X Z7Z Z83 Z88 ZMTXR ~A9 ~WA -SI -S~ 0R~ AACDK AAJBT AASML AAXDM AAYZH ABAKF ABQSL ACDTI AEFQL AEMSY AGQEE AGRTI AIGIU BSONS CAJEI CJPJV H13 Q-- U1G U5S AAPKM AAYXX ABBRH ABDBE ABRTQ ADHKG AFDZB AFFHD AFOHR AGQPQ AHPBZ ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 8FE 8FG AZQEC DWQXO GNUQQ JQ2 P62 PKEHL PQEST PQQKQ PQUKI 7SC 8FD L7M L~C L~D PUEGO |
| ID | FETCH-LOGICAL-c376t-f8d54d717d065ddf20d7f8b12ba6d2a2aee7f082cae1a028e76722901769fb033 |
| IEDL.DBID | P5Z |
| ISICitedReferencesCount | 85 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000343081700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1674-733X |
| IngestDate | Thu Sep 04 16:57:33 EDT 2025 Fri Nov 07 23:36:05 EST 2025 Sat Nov 29 03:42:13 EST 2025 Tue Nov 18 21:42:58 EST 2025 Fri Feb 21 02:34:11 EST 2025 Wed Feb 14 10:38:40 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Keywords | fuzziness parameter cluster validity index clustering cluster validation fuzzy c-means (FCM) |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c376t-f8d54d717d065ddf20d7f8b12ba6d2a2aee7f082cae1a028e76722901769fb033 |
| Notes | 11-5847/TP clustering, fuzziness parameter, fuzzy c-means （FCM）, cluster validation, cluster validity index Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index （CVI） is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3]. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PQID | 2918529656 |
| PQPubID | 2043626 |
| PageCount | 8 |
| ParticipantIDs | proquest_miscellaneous_1651412697 proquest_journals_2918529656 crossref_citationtrail_10_1007_s11432_014_5146_0 crossref_primary_10_1007_s11432_014_5146_0 springer_journals_10_1007_s11432_014_5146_0 chongqing_primary_662507257 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-11-01 |
| PublicationDateYYYYMMDD | 2014-11-01 |
| PublicationDate_xml | – month: 11 year: 2014 text: 2014-11-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Heidelberg |
| PublicationPlace_xml | – name: Heidelberg |
| PublicationTitle | Science China. Information sciences |
| PublicationTitleAbbrev | Sci. China Inf. Sci |
| PublicationTitleAlternate | SCIENCE CHINA Information Sciences |
| PublicationYear | 2014 |
| Publisher | Science China Press Springer Nature B.V |
| Publisher_xml | – name: Science China Press – name: Springer Nature B.V |
| References | CannonR LDaveJ VBezdekJ CEfficient implementation of the fuzzy c-means clustering algorithmsIEEE Trans Pattern Anal1986PAMI-824825510.1109/TPAMI.1986.4767778 HuangMXiaZWangHThe range of the value for the fuzzifier of the fuzzy c-means algorithmPattern Recogn Lett2012332280228410.1016/j.patrec.2012.08.014 KimMRamakrishnaR SNew indices for cluster validity assessmentPattern Recogn Lett2005262353236310.1016/j.patrec.2005.04.007 ZhangY JWangW NZhangX NA cluster validity index for fuzzy clusteringInfo Sci20081781205121810.1016/j.ins.2007.10.0041134.68050 WuK LAnalysis of parameter selections for fuzzy C-meansPattern Recogn20124540741510.1016/j.patcog.2011.07.0121225.68237 MaulikUBandyopadhyaySPerformance evaluation of some clustering algorithms and validity indicesIEEE Trans Pattern Anal2002241650165410.1109/TPAMI.2002.1114856 XieX LBeniGA validity measure for fuzzy clusteringIEEE Trans Pattern Anal19911384184710.1109/34.85677 ZalikK RCluster validity index for estimation of fuzzy clusters of different sizes and densitiesPattern Recogn2010433374339010.1016/j.patcog.2010.04.0251213.68558 DunnCA fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clustersJ Cybern19743325737585710.1080/01969727308546046 KwonS HCluster validity index for fuzzy clusteringElectron Lett1998342176217710.1049/el:19981523 GurrutxagaIMuguerzaJArbelaitzOTowards a standard methodology to evaluate internal cluster validity indicesPattern Recogn Lett20113250551510.1016/j.patrec.2010.11.006 DimitriadouEDolňicarSWeingesselAAn examination of indexes for determining the number of clusters in binary data setsPsychometrika200267137159196083210.1007/BF02294713 YueS HWuTCuiL JClustering mechanism for electric tomography imagingSci China Inf Sci20125528492864301104510.1007/s11432-012-4748-7 HoppnerFKlawonFKruseRFuzzy Cluster Analysis: Methods for Classifications Data Analysis and Image Recognition1999New YorkWiley Jegatha DeborahLBaskaranRKannanAA survey on internal validity measure for cluster validationInt J Computer Sci Eng Surv201018510210.5121/ijcses.2010.1207 GuerraLRoblesVBielzaCA comparison of clustering quality indices using outliers and noiseIntell Data Anal201216703715 WangWZhangYOn fuzzy cluster validity indicesFuzzy Set Syst20071582095211710.1016/j.fss.2007.03.0041123.62046 BensaidA MHallL OBezdekJ CValidity-guided (Re)clustering with applications to image segmentationIEEE Trans Fuzzy Syst1996411212310.1109/91.493905 FrankAAsuncionAUCI Machine Learning Repository2010CaliforniaUniversity of California ArbelaitzOGurrutxagaIMuguerzaJAn extensive comparative study of cluster validity indicesPattern Recogn20134624325610.1016/j.patcog.2012.07.021 JainA KData clustering: 50 years beyond k-meansPattern Recogn Lett20103165166610.1016/j.patrec.2009.09.011 DevijverP AKittlerJPattern Recognition: A Statistical Approach1982LondonPrentice-Hall0542.68071 HartiganJ AClustering Algorithms1975New YorkWiley0372.62040 YuJOn the fuzziness index of the FCM algorithmsChin J Comput200326965973 NiW WChongZ HClustering-oriented privacy-preserving data publishingKnowl-based Syst20123526427010.1016/j.knosys.2012.05.012 YuJChengQHuangHAnalysis of the weighting exponent in the FCMIEEE Trans Syst Man Cy B20043463463910.1109/TSMCB.2003.810951 BezdekJ CPattern recognition with fuzzy objective function algorithms1981New YorkPlenum Press10.1007/978-1-4757-0450-10503.68069 GevaA BSteinbergYBruckmairSA comparison of cluster validity criteria for a mixture of normal distributed dataPattern Recogn Lett20002151152910.1016/S0167-8655(00)00016-7 ChanK PCheungY SClustering of clustersPattern Recogn19922521121710.1016/0031-3203(92)90102-O ChoeHJordanJ BOn the optimal choice of parameters in a fuzzy c-means algorithmProceedings of IEEE International Conference on Fuzzy Systems, IEEE Computer Society199234935410.1109/FUZZY.1992.258640 XuRWunschDIISurvey of clustering algorithmsIEEE Trans Neural Networ20051664567810.1109/TNN.2005.845141 OzkanITurksenI BEntropy assessment for type-2 fuzzinessProceedings of IEEE International Conference on Fuzzy Systems, IEEE Computer Society200411111115 HuC XLiuY MLiGImproved FOCUSS method for reconstruction of cluster structured sparse signals in radar imagingSci China Ser F-Inf Sci20125517761788296006710.1007/s11432-012-4628-11270.94017 HallL LBensaidA MClarkeL PA comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brainIEEE Trans Neural Networ2002367268210.1109/72.159057 ShenYShiHZhangJ QImprovement and optimization of a fuzzy c-means clustering algorithmProceedings of the 18th Instrumentation and Measurement Technology Conference, IEEE Computer Society200114301433 PalN RBezdekJ COn cluster validity for the fuzzy c-mean modelIEEE Trans Fuzzy Syst1995337037910.1109/91.413225 FadiliM JRuanSBloyetDOn the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time seriesMed Image Anal20015556710.1016/S1361-8415(00)00035-9 PalN RBezdekJ CCorrection to on cluster validity for the fuzzy c-means modelIEEE Trans Fuzzy Syst1997515215310.1109/TFUZZ.1997.554463 BezdekJ CA physical interpretation of fuzzy ISODATAIEEE Trans Syst Man Cy B1976SMC-6387390418548 ZalikK RZalikBValidity index for clusters of different sizes and densitiesPattern Recogn Lett20113222123410.1016/j.patrec.2010.08.007 OzkanITurksenI BUpper and lower values for the level of fuzziness in FCMInform Sci20071775143515210.1016/j.ins.2007.06.0281126.68610 BezdekJ CHathawayRConvergence theory for fuzzy c-means: Counterexamples and repairsIEEE Trans Syst Man Cy B19871787387710.1109/TSMC.1987.64992960653.68091 TangYSunFSunZImproved validation index for fuzzy clusteringProceedings of the 2005 American Control Conference, IEEE Computer Society200511201125 HwangCRheeF C HUncertain fuzzy clustering: Interval type-2 fuzzy approach to c-meansIEEE Trans Fuzzy Syst20071510712010.1109/TFUZZ.2006.889763 BezdekJ CEhrlishRFullWFCM: The fuzzy c-means clustering algorithmComput Geosci-UK19841019120310.1016/0098-3004(84)90020-7 O Arbelaitz (5146_CR38) 2013; 46 I Ozkan (5146_CR17) 2004 M Kim (5146_CR27) 2005; 26 E Dimitriadou (5146_CR43) 2002; 67 H Choe (5146_CR16) 1992 X L Xie (5146_CR29) 1991; 13 A K Jain (5146_CR3) 2010; 31 J Yu (5146_CR22) 2003; 26 A M Bensaid (5146_CR32) 1996; 4 Y Tang (5146_CR31) 2005 N R Pal (5146_CR9) 1995; 3 A B Geva (5146_CR42) 2000; 21 C Hwang (5146_CR21) 2007; 15 L Jegatha Deborah (5146_CR36) 2010; 1 K R Zalik (5146_CR40) 2011; 32 S H Kwon (5146_CR30) 1998; 34 L L Hall (5146_CR10) 2002; 3 Y J Zhang (5146_CR35) 2008; 178 K R Zalik (5146_CR41) 2010; 43 W W Ni (5146_CR6) 2012; 35 J C Bezdek (5146_CR8) 1981 P A Devijver (5146_CR25) 1982 R Xu (5146_CR4) 2005; 16 K P Chan (5146_CR15) 1992; 25 W Wang (5146_CR28) 2007; 158 L Guerra (5146_CR37) 2012; 16 U Maulik (5146_CR44) 2002; 24 M J Fadili (5146_CR24) 2001; 5 J C Bezdek (5146_CR14) 1987; 17 N R Pal (5146_CR45) 1997; 5 S H Yue (5146_CR2) 2012; 55 Y Shen (5146_CR12) 2001 R L Cannon (5146_CR11) 1986; PAMI-8 J A Hartigan (5146_CR1) 1975 A Frank (5146_CR34) 2010 C X Hu (5146_CR5) 2012; 55 I Gurrutxaga (5146_CR39) 2011; 32 C Dunn (5146_CR7) 1974; 3 J C Bezdek (5146_CR13) 1976; SMC-6 J Yu (5146_CR23) 2004; 34 M Huang (5146_CR20) 2012; 33 F Hoppner (5146_CR26) 1999 K L Wu (5146_CR19) 2012; 45 J C Bezdek (5146_CR33) 1984; 10 I Ozkan (5146_CR18) 2007; 177 |
| References_xml | – reference: YuJOn the fuzziness index of the FCM algorithmsChin J Comput200326965973 – reference: ZalikK RCluster validity index for estimation of fuzzy clusters of different sizes and densitiesPattern Recogn2010433374339010.1016/j.patcog.2010.04.0251213.68558 – reference: FrankAAsuncionAUCI Machine Learning Repository2010CaliforniaUniversity of California – reference: DunnCA fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clustersJ Cybern19743325737585710.1080/01969727308546046 – reference: FadiliM JRuanSBloyetDOn the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time seriesMed Image Anal20015556710.1016/S1361-8415(00)00035-9 – reference: XuRWunschDIISurvey of clustering algorithmsIEEE Trans Neural Networ20051664567810.1109/TNN.2005.845141 – reference: DimitriadouEDolňicarSWeingesselAAn examination of indexes for determining the number of clusters in binary data setsPsychometrika200267137159196083210.1007/BF02294713 – reference: HallL LBensaidA MClarkeL PA comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brainIEEE Trans Neural Networ2002367268210.1109/72.159057 – reference: OzkanITurksenI BEntropy assessment for type-2 fuzzinessProceedings of IEEE International Conference on Fuzzy Systems, IEEE Computer Society200411111115 – reference: PalN RBezdekJ COn cluster validity for the fuzzy c-mean modelIEEE Trans Fuzzy Syst1995337037910.1109/91.413225 – reference: BensaidA MHallL OBezdekJ CValidity-guided (Re)clustering with applications to image segmentationIEEE Trans Fuzzy Syst1996411212310.1109/91.493905 – reference: BezdekJ CEhrlishRFullWFCM: The fuzzy c-means clustering algorithmComput Geosci-UK19841019120310.1016/0098-3004(84)90020-7 – reference: JainA KData clustering: 50 years beyond k-meansPattern Recogn Lett20103165166610.1016/j.patrec.2009.09.011 – reference: Jegatha DeborahLBaskaranRKannanAA survey on internal validity measure for cluster validationInt J Computer Sci Eng Surv201018510210.5121/ijcses.2010.1207 – reference: XieX LBeniGA validity measure for fuzzy clusteringIEEE Trans Pattern Anal19911384184710.1109/34.85677 – reference: TangYSunFSunZImproved validation index for fuzzy clusteringProceedings of the 2005 American Control Conference, IEEE Computer Society200511201125 – reference: KimMRamakrishnaR SNew indices for cluster validity assessmentPattern Recogn Lett2005262353236310.1016/j.patrec.2005.04.007 – reference: HartiganJ AClustering Algorithms1975New YorkWiley0372.62040 – reference: HoppnerFKlawonFKruseRFuzzy Cluster Analysis: Methods for Classifications Data Analysis and Image Recognition1999New YorkWiley – reference: DevijverP AKittlerJPattern Recognition: A Statistical Approach1982LondonPrentice-Hall0542.68071 – reference: HuC XLiuY MLiGImproved FOCUSS method for reconstruction of cluster structured sparse signals in radar imagingSci China Ser F-Inf Sci20125517761788296006710.1007/s11432-012-4628-11270.94017 – reference: YuJChengQHuangHAnalysis of the weighting exponent in the FCMIEEE Trans Syst Man Cy B20043463463910.1109/TSMCB.2003.810951 – reference: WangWZhangYOn fuzzy cluster validity indicesFuzzy Set Syst20071582095211710.1016/j.fss.2007.03.0041123.62046 – reference: ZalikK RZalikBValidity index for clusters of different sizes and densitiesPattern Recogn Lett20113222123410.1016/j.patrec.2010.08.007 – reference: MaulikUBandyopadhyaySPerformance evaluation of some clustering algorithms and validity indicesIEEE Trans Pattern Anal2002241650165410.1109/TPAMI.2002.1114856 – reference: BezdekJ CPattern recognition with fuzzy objective function algorithms1981New YorkPlenum Press10.1007/978-1-4757-0450-10503.68069 – reference: GurrutxagaIMuguerzaJArbelaitzOTowards a standard methodology to evaluate internal cluster validity indicesPattern Recogn Lett20113250551510.1016/j.patrec.2010.11.006 – reference: CannonR LDaveJ VBezdekJ CEfficient implementation of the fuzzy c-means clustering algorithmsIEEE Trans Pattern Anal1986PAMI-824825510.1109/TPAMI.1986.4767778 – reference: ChanK PCheungY SClustering of clustersPattern Recogn19922521121710.1016/0031-3203(92)90102-O – reference: WuK LAnalysis of parameter selections for fuzzy C-meansPattern Recogn20124540741510.1016/j.patcog.2011.07.0121225.68237 – reference: PalN RBezdekJ CCorrection to on cluster validity for the fuzzy c-means modelIEEE Trans Fuzzy Syst1997515215310.1109/TFUZZ.1997.554463 – reference: NiW WChongZ HClustering-oriented privacy-preserving data publishingKnowl-based Syst20123526427010.1016/j.knosys.2012.05.012 – reference: BezdekJ CA physical interpretation of fuzzy ISODATAIEEE Trans Syst Man Cy B1976SMC-6387390418548 – reference: GevaA BSteinbergYBruckmairSA comparison of cluster validity criteria for a mixture of normal distributed dataPattern Recogn Lett20002151152910.1016/S0167-8655(00)00016-7 – reference: BezdekJ CHathawayRConvergence theory for fuzzy c-means: Counterexamples and repairsIEEE Trans Syst Man Cy B19871787387710.1109/TSMC.1987.64992960653.68091 – reference: HuangMXiaZWangHThe range of the value for the fuzzifier of the fuzzy c-means algorithmPattern Recogn Lett2012332280228410.1016/j.patrec.2012.08.014 – reference: HwangCRheeF C HUncertain fuzzy clustering: Interval type-2 fuzzy approach to c-meansIEEE Trans Fuzzy Syst20071510712010.1109/TFUZZ.2006.889763 – reference: ChoeHJordanJ BOn the optimal choice of parameters in a fuzzy c-means algorithmProceedings of IEEE International Conference on Fuzzy Systems, IEEE Computer Society199234935410.1109/FUZZY.1992.258640 – reference: OzkanITurksenI BUpper and lower values for the level of fuzziness in FCMInform Sci20071775143515210.1016/j.ins.2007.06.0281126.68610 – reference: GuerraLRoblesVBielzaCA comparison of clustering quality indices using outliers and noiseIntell Data Anal201216703715 – reference: ShenYShiHZhangJ QImprovement and optimization of a fuzzy c-means clustering algorithmProceedings of the 18th Instrumentation and Measurement Technology Conference, IEEE Computer Society200114301433 – reference: KwonS HCluster validity index for fuzzy clusteringElectron Lett1998342176217710.1049/el:19981523 – reference: YueS HWuTCuiL JClustering mechanism for electric tomography imagingSci China Inf Sci20125528492864301104510.1007/s11432-012-4748-7 – reference: ZhangY JWangW NZhangX NA cluster validity index for fuzzy clusteringInfo Sci20081781205121810.1016/j.ins.2007.10.0041134.68050 – reference: ArbelaitzOGurrutxagaIMuguerzaJAn extensive comparative study of cluster validity indicesPattern Recogn20134624325610.1016/j.patcog.2012.07.021 – volume: 177 start-page: 5143 year: 2007 ident: 5146_CR18 publication-title: Inform Sci doi: 10.1016/j.ins.2007.06.028 – volume-title: UCI Machine Learning Repository year: 2010 ident: 5146_CR34 – start-page: 1430 volume-title: Proceedings of the 18th Instrumentation and Measurement Technology Conference, IEEE Computer Society year: 2001 ident: 5146_CR12 – volume: 31 start-page: 651 year: 2010 ident: 5146_CR3 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2009.09.011 – volume-title: Fuzzy Cluster Analysis: Methods for Classifications Data Analysis and Image Recognition year: 1999 ident: 5146_CR26 – volume: 16 start-page: 703 year: 2012 ident: 5146_CR37 publication-title: Intell Data Anal doi: 10.3233/IDA-2012-0545 – volume: 24 start-page: 1650 year: 2002 ident: 5146_CR44 publication-title: IEEE Trans Pattern Anal doi: 10.1109/TPAMI.2002.1114856 – volume: 25 start-page: 211 year: 1992 ident: 5146_CR15 publication-title: Pattern Recogn doi: 10.1016/0031-3203(92)90102-O – volume: 3 start-page: 672 year: 2002 ident: 5146_CR10 publication-title: IEEE Trans Neural Networ doi: 10.1109/72.159057 – volume: 32 start-page: 505 year: 2011 ident: 5146_CR39 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2010.11.006 – volume: 67 start-page: 137 year: 2002 ident: 5146_CR43 publication-title: Psychometrika doi: 10.1007/BF02294713 – volume: 33 start-page: 2280 year: 2012 ident: 5146_CR20 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2012.08.014 – volume-title: Pattern recognition with fuzzy objective function algorithms year: 1981 ident: 5146_CR8 doi: 10.1007/978-1-4757-0450-1 – volume: 26 start-page: 965 year: 2003 ident: 5146_CR22 publication-title: Chin J Comput – volume: 55 start-page: 1776 year: 2012 ident: 5146_CR5 publication-title: Sci China Ser F-Inf Sci – volume-title: Pattern Recognition: A Statistical Approach year: 1982 ident: 5146_CR25 – volume: 34 start-page: 2176 year: 1998 ident: 5146_CR30 publication-title: Electron Lett doi: 10.1049/el:19981523 – volume: 5 start-page: 55 year: 2001 ident: 5146_CR24 publication-title: Med Image Anal doi: 10.1016/S1361-8415(00)00035-9 – volume: 34 start-page: 634 year: 2004 ident: 5146_CR23 publication-title: IEEE Trans Syst Man Cy B doi: 10.1109/TSMCB.2003.810951 – volume: 13 start-page: 841 year: 1991 ident: 5146_CR29 publication-title: IEEE Trans Pattern Anal doi: 10.1109/34.85677 – volume: 15 start-page: 107 year: 2007 ident: 5146_CR21 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2006.889763 – volume: 26 start-page: 2353 year: 2005 ident: 5146_CR27 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2005.04.007 – volume-title: Clustering Algorithms year: 1975 ident: 5146_CR1 – volume: SMC-6 start-page: 387 year: 1976 ident: 5146_CR13 publication-title: IEEE Trans Syst Man Cy B doi: 10.1109/TSMC.1976.4309506 – volume: 17 start-page: 873 year: 1987 ident: 5146_CR14 publication-title: IEEE Trans Syst Man Cy B doi: 10.1109/TSMC.1987.6499296 – volume: 10 start-page: 191 year: 1984 ident: 5146_CR33 publication-title: Comput Geosci-UK doi: 10.1016/0098-3004(84)90020-7 – volume: 32 start-page: 221 year: 2011 ident: 5146_CR40 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2010.08.007 – volume: 45 start-page: 407 year: 2012 ident: 5146_CR19 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2011.07.012 – start-page: 1120 volume-title: Proceedings of the 2005 American Control Conference, IEEE Computer Society year: 2005 ident: 5146_CR31 – volume: 21 start-page: 511 year: 2000 ident: 5146_CR42 publication-title: Pattern Recogn Lett doi: 10.1016/S0167-8655(00)00016-7 – volume: 3 start-page: 370 year: 1995 ident: 5146_CR9 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/91.413225 – volume: 158 start-page: 2095 year: 2007 ident: 5146_CR28 publication-title: Fuzzy Set Syst doi: 10.1016/j.fss.2007.03.004 – volume: PAMI-8 start-page: 248 year: 1986 ident: 5146_CR11 publication-title: IEEE Trans Pattern Anal doi: 10.1109/TPAMI.1986.4767778 – volume: 16 start-page: 645 year: 2005 ident: 5146_CR4 publication-title: IEEE Trans Neural Networ doi: 10.1109/TNN.2005.845141 – volume: 1 start-page: 85 year: 2010 ident: 5146_CR36 publication-title: Int J Computer Sci Eng Surv doi: 10.5121/ijcses.2010.1207 – volume: 46 start-page: 243 year: 2013 ident: 5146_CR38 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2012.07.021 – volume: 55 start-page: 2849 year: 2012 ident: 5146_CR2 publication-title: Sci China Inf Sci doi: 10.1007/s11432-012-4748-7 – volume: 5 start-page: 152 year: 1997 ident: 5146_CR45 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.1997.554463 – start-page: 349 volume-title: Proceedings of IEEE International Conference on Fuzzy Systems, IEEE Computer Society year: 1992 ident: 5146_CR16 doi: 10.1109/FUZZY.1992.258640 – volume: 4 start-page: 112 year: 1996 ident: 5146_CR32 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/91.493905 – volume: 35 start-page: 264 year: 2012 ident: 5146_CR6 publication-title: Knowl-based Syst doi: 10.1016/j.knosys.2012.05.012 – start-page: 1111 volume-title: Proceedings of IEEE International Conference on Fuzzy Systems, IEEE Computer Society year: 2004 ident: 5146_CR17 – volume: 43 start-page: 3374 year: 2010 ident: 5146_CR41 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2010.04.025 – volume: 3 start-page: 32 year: 1974 ident: 5146_CR7 publication-title: J Cybern doi: 10.1080/01969727308546046 – volume: 178 start-page: 1205 year: 2008 ident: 5146_CR35 publication-title: Info Sci doi: 10.1016/j.ins.2007.10.004 |
| SSID | ssj0000330278 |
| Score | 2.3245807 |
| Snippet | Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter... Fuzzy c-means (FCM) algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m , in FCM algorithm is a key parameter... Fuzzy c-means (FCM) algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter... |
| SourceID | proquest crossref springer chongqing |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 247 |
| SubjectTerms | Algorithms Clustering Clusters Computer Science Datasets FCM算法 Fuzzy Fuzzy logic Fuzzy set theory Information Systems and Communication Service Optimization Parameters Pattern recognition Research Paper Synthetic data 参数选择 有效性指数 模糊c均值 群集 聚类方法 聚类结果 验证 |
| SummonAdditionalLinks | – databaseName: Springer Nature Link Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8QwEA6-DnpwfeK6KhE8KYU2bZOtNxHFg4j4Ym-heVQXdlu1u4L-eme6faiooOdOJyEzyUwyM98Qshcp3_ih9ZyI4dNNZLWjtFGOMCZEZEimCjCdu3NxcdHt9aLLso47r7Ldq5BkcVI3xW5g2jGNIHDAyMM1eJrMgrXrYr-Gq-u7-mHF9TEUV5TAccw19P1eFc38jgtiKjxk6f0TjPjZNjUO55cYaWF6Tlv_mvQSWSw9TXo0UY1lMmXTFdKqujjQclOvkIUPkISr5PZ0_PZWpMJTRAUfYrYMzYtmOSBB2k9pAgSvVDtDC1bukIKe0cemYpNmCdWDMcIvUFDi_qRl0xrwPbk5PnPK1guOhhNn5CRdEwYGrnoGXBRjEuYakXSVx1TMDYtZbK1IwHvQsfVicFGs4AKR4z3Bo0SBCNbJTJqldoNQzV0DEkl0HPDABkCuvFBZy4XmGtyRNunUApCPE4gNyeFa5go4TtrErUQidYlajs0zBrLBW8YllrDEEpdYAsP9-peK3y_EW5WcZbl7c8kiLCmPwNVtk936M-w7DKbEqc3GufQ4MPAYj2CKB5XsGxY_Drj5J-oOmWeoPEXx4xaZGT2P7TaZ0y-jfv68Uyj-O2Z_-6Q priority: 102 providerName: Springer Nature |
| Title | Fuzziness parameter selection in fuzzy c-means： The perspective of cluster validation |
| URI | http://lib.cqvip.com/qk/84009A/201411/662507257.html https://link.springer.com/article/10.1007/s11432-014-5146-0 https://www.proquest.com/docview/2918529656 https://www.proquest.com/docview/1651412697 |
| Volume | 57 |
| WOSCitedRecordID | wos000343081700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1869-1919 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0000330278 issn: 1674-733X databaseCode: P5Z dateStart: 20010201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1869-1919 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0000330278 issn: 1674-733X databaseCode: K7- dateStart: 20010201 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1869-1919 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0000330278 issn: 1674-733X databaseCode: BENPR dateStart: 20010201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: Springer Nature Link Contemporary 1997-Present customDbUrl: eissn: 1869-1919 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000330278 issn: 1674-733X databaseCode: RSV dateStart: 20100101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB71wQEOUF5iabsyEieQReIk9qaXClArJNBqVaBacbHiR2ClNtk2u0j01zPjTTa0UnvhkkucceQZ2589M98AvM5N4pLMxzwXdHWTe8uNdYYr5zJihhQmkOmcflHj8Wg6zSfthVvThlV2a2JYqF1t6Y78ncgpzTdH-HE4v-BUNYq8q20JjU3YJpYEKt0wyX6s71iihLxyIRtOUthhkkw7x2bInkOsQHEJKUfUgOdqolf4VVc_L3DTuL5N9djzhrs07ELHj_73_3fgYYs_2fuVwTyGDV89gQf_sBI-he_Hy6urEA3PiBj8nAJmWBPq5aAS2axiJTb4wyw_97jRHTA0NTbvkzZZXTJ7tiQGBoZ2PFtVbXqGco--ffzE2-oL3OKis-DlyGWpw9OeQ5TiXCkip8qRiYUppBOFKLxXJQIIW_i4QJTilVREHh8rmZcGh_45bFV15V8AszJyqInSFqlMfYrNTZwZ76Wy0iIiGcDueuD1fMWyoSWezCKFK8oAok4V2rbE5VQ_40z3lMukSY2a1KRJjQLfrD_p5N3ReK_TmG4ncKN7dQ3g1fo1Tj3ypxSVr5eNjiUKiIXM8RffdnbRi7i1w5d3d7gL9wUZZUh43IOtxeXS78M9-3sxay6HsP3haDw5GcLmZ8WHwd7xefL19C_8bQPe |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9tAEB4hQGo5AH0gApRupfbSaoW9dnbjSlVV0UagpFEPtMpt630YIoEdcNIKflR_IzNOHLeVyo1Dz16PHzvPnZlvAF4mJnJR24c8EXR0k3jLjXWGK-fahAwpTAWm862vBoPOcJh8WYJfdS8MlVXWOrFS1K6wdEZ-IBJq803Q_Xg_vuQ0NYqyq_UIjRlb9Pz1TwzZynfHH3F_XwnR_XRyeMTnUwW4RWGa8Kzj2rHDKMah9XUuE4FTWceEwqTSiVSk3qsMDaNNfZii9fVKKgJFD5VMMhPQASiq_JU46iiSq57iizMdvEiJPIrxJJU5RtGwTqRW3Xrom1AdRMzRS8E4nuAczor89BKN1J9msfF1_0rPVlavu_G__a9NWJ_71-zDTCAewZLPH8Pab6iLT-Brd3pzU1X7MwI-v6CCIFZW84CQSdkoZxkuuGaWX3g05G8ZihIbN02prMiYPZ8SwgRDOR3NplI9Rbr38V1bsJwXud8GZmXgcOczm8Yy9jEuN2HbeC-VlRY9rhbsLjZaj2coIlpi5Bko1JgtCOqt13YOzE7zQc51AylNnKORczRxjkaCrxe31PTuWLxXc4ieK6hSN-zRgheLy6haKF-U5r6YljqUSCAUMsFXfFPzYUPinw_cufuBz-HB0cnnvu4fD3q78FCQQFTNnXuwPLma-mewan9MRuXVfiVdDL7fN3veAjYMXSI |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3da9tADBdrO8b2sH5tNP28wp5aTO2zcxf3bWwLLS2h0K3k7fB9dYHWSeNksPz1kxw77sY6GH32WWefpJPuJP0E8CHVsY3bLgpSTlc3qTOBNlYH0to2IUNyXYLp3FzKXq_T76dXVZ_Tos52r0OS85oGQmnKJycj60-awjc085RSkARo8PFIvAQrCeXR03H9-mZxyRLGFJYry-EE5R3Gcb-ObP6NCuErfB_mtw84--92qnE-_4iXlmaou_rsH1iDt5UHyj7ORWYdXrh8A1br7g6sUvYNePMIqnATvnWns1mZIs8ILfyesmhYUTbRQc6yQc48DvjJTHDv0PqdMpQ_NmoqOdnQM3M3JVgGhsI9mLdyeod0v3z9dBZULRkCgzvRJPAd204sHgEtui7Weh5a6Ts64joTlmc8c0569CpM5qIMXRcnhSRE-UiK1Gtkx3tYzoe52wJmRGiRO95kiUhcgsN11NbOCWmEQTelBTsLZqjRHHpDCTyuhRK3mRaENXuUqdDMqanGnWpwmGmJFS6xoiVWSPBo8UpN7x-Dd2ueq0qrC8VTKjVP0QVuweHiMeojBVmy3A2nhYoEEoi4SPETj2s5aEg8OeH2f40-gFdXn7vq8rx3sQOvOclRWR-5C8uT8dTtwUvzYzIoxvulPvwC1m8Hew |
| 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=Fuzziness+parameter+selection+in+fuzzy+c-means%3A+The+perspective+of+cluster+validation&rft.jtitle=Science+China.+Information+sciences&rft.au=Zhou%2C+KaiLe&rft.au=Fu%2C+Chao&rft.au=Yang%2C+ShanLin&rft.date=2014-11-01&rft.issn=1674-733X&rft.eissn=1869-1919&rft.volume=57&rft.issue=11&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1007%2Fs11432-014-5146-0&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_s | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F84009A%2F84009A.jpg |