A possibilistic fuzzy Gath-Geva clustering algorithm using the exponential distance
[Display omitted] •Propose PFGG clustering algorithm.•PFGG uses the exponential distance in contrast to PFCM.•PFGG clusters the dataset containing noisy data accurately.•The clustering accuracies, cluster centers and convergence results of PFGG are significantly better than FCM, GG, PFCM. As a famou...
Saved in:
| Published in: | Expert systems with applications Vol. 184; p. 115550 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Elsevier Ltd
01.12.2021
Elsevier BV |
| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | [Display omitted]
•Propose PFGG clustering algorithm.•PFGG uses the exponential distance in contrast to PFCM.•PFGG clusters the dataset containing noisy data accurately.•The clustering accuracies, cluster centers and convergence results of PFGG are significantly better than FCM, GG, PFCM.
As a famous clustering algorithm, Gath-Geva (GG) clustering has been widely applied in many fields. Unlike fuzzy c-means (FCM) clustering, GG clustering uses the exponential distance with the fuzzy covariance matrix instead of Euclidean distance to cluster hyperellipsoidal data accurately. Due to the existence of probabilistic constraint, GG clustering is sensitive to noise data. For possibilistic fuzzy c-means (PFCM), the type of clustering data limits its application. To solve the noise sensitivity problem of GG clustering and extend PFCM for clustering hyperellipsoidal data, a possibilistic fuzzy Gath-Geva (PFGG) clustering algorithm is proposed. The PFGG clustering is derived from GG clustering in combination with PFCM clustering. FCM, GG, PFCM and PFGG are run on the several datasets to compare their clustering results. The experimental results show that the performance of PFGG is significantly better than the other clustering algorithms. |
|---|---|
| AbstractList | As a famous clustering algorithm, Gath-Geva (GG) clustering has been widely applied in many fields. Unlike fuzzy c-means (FCM) clustering, GG clustering uses the exponential distance with the fuzzy covariance matrix instead of Euclidean distance to cluster hyperellipsoidal data accurately. Due to the existence of probabilistic constraint, GG clustering is sensitive to noise data. For possibilistic fuzzy c-means (PFCM), the type of clustering data limits its application. To solve the noise sensitivity problem of GG clustering and extend PFCM for clustering hyperellipsoidal data, a possibilistic fuzzy Gath-Geva (PFGG) clustering algorithm is proposed. The PFGG clustering is derived from GG clustering in combination with PFCM clustering. FCM, GG, PFCM and PFGG are run on the several datasets to compare their clustering results. The experimental results show that the performance of PFGG is significantly better than the other clustering algorithms. [Display omitted] •Propose PFGG clustering algorithm.•PFGG uses the exponential distance in contrast to PFCM.•PFGG clusters the dataset containing noisy data accurately.•The clustering accuracies, cluster centers and convergence results of PFGG are significantly better than FCM, GG, PFCM. As a famous clustering algorithm, Gath-Geva (GG) clustering has been widely applied in many fields. Unlike fuzzy c-means (FCM) clustering, GG clustering uses the exponential distance with the fuzzy covariance matrix instead of Euclidean distance to cluster hyperellipsoidal data accurately. Due to the existence of probabilistic constraint, GG clustering is sensitive to noise data. For possibilistic fuzzy c-means (PFCM), the type of clustering data limits its application. To solve the noise sensitivity problem of GG clustering and extend PFCM for clustering hyperellipsoidal data, a possibilistic fuzzy Gath-Geva (PFGG) clustering algorithm is proposed. The PFGG clustering is derived from GG clustering in combination with PFCM clustering. FCM, GG, PFCM and PFGG are run on the several datasets to compare their clustering results. The experimental results show that the performance of PFGG is significantly better than the other clustering algorithms. |
| ArticleNumber | 115550 |
| Author | Wu, Xiaohong Wu, Bin Zhou, Haoxiang Zhang, Tingfei |
| Author_xml | – sequence: 1 givenname: Xiaohong surname: Wu fullname: Wu, Xiaohong email: wxh_www@163.com organization: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China – sequence: 2 givenname: Haoxiang surname: Zhou fullname: Zhou, Haoxiang email: 1377099026@qq.com organization: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China – sequence: 3 givenname: Bin surname: Wu fullname: Wu, Bin email: wubin2003@163.com organization: Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, China – sequence: 4 givenname: Tingfei surname: Zhang fullname: Zhang, Tingfei email: 672816958@qq.com organization: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China |
| BookMark | eNp9kDtPwzAQgC1UJNrCH2CKxJxgOw8nEktVQUFCYgBm6-LYraM0CbZTaH89jsLE0Ol0uvvu8S3QrO1aidAtwRHBJLuvI2m_IaKYkoiQNE3xBZqTnMVhxop4hua4SFmYEJZcoYW1NcaEYczm6H0V9J21utSNtk6LQA2n0zHYgNuFG3mAQDSDddLodhtAs-2Mdrt9MNgxdzsZyJ_eX9I6DU1Q-QnQCnmNLhU0Vt78xSX6fHr8WD-Hr2-bl_XqNRQ0zV2olD8BsrysMCtVLFIMNKlSIJmKC8C5SJMYGIulwuBzWfoyZbgsSlKxMoF4ie6mub3pvgZpHa-7wbR-JfcLKKWY5IXvolOXMP5RIxXvjd6DOXKC-SiP13yUx0d5fJLnofwfJLQDp7vWGdDNefRhQqV__aCl4VZo6bVU2kjheNXpc_gv8HiNmQ |
| CitedBy_id | crossref_primary_10_3233_JIFS_223854 crossref_primary_10_1016_j_measurement_2022_111683 crossref_primary_10_3233_JIFS_223576 crossref_primary_10_38124_ijisrt_25aug324 crossref_primary_10_1155_2022_8562194 crossref_primary_10_1155_2024_5806437 crossref_primary_10_3390_s21227502 crossref_primary_10_1016_j_energy_2023_126703 crossref_primary_10_1016_j_aei_2024_102772 crossref_primary_10_3390_app15084479 crossref_primary_10_1088_1361_6501_ad0ca6 crossref_primary_10_1093_comjnl_bxaf081 crossref_primary_10_1016_j_knosys_2023_110261 |
| Cites_doi | 10.1016/j.engappai.2019.07.015 10.1111/jfs.12555 10.1016/j.apm.2014.11.041 10.1109/TSMC.2017.2735995 10.3390/foods8010038 10.1016/j.apm.2012.04.031 10.1111/jfpe.12355 10.1109/TSMCB.2002.1033180 10.1016/j.compag.2018.02.014 10.1016/j.compmedimag.2010.12.001 10.1016/j.compag.2019.03.004 10.1016/j.asoc.2016.12.049 10.1016/j.asoc.2019.105610 10.1016/j.renene.2019.08.064 10.1016/j.apacoust.2017.01.023 10.1016/j.imavis.2019.02.006 10.1002/jsfa.9943 10.1016/j.ins.2019.07.100 10.1016/j.techfore.2019.05.015 10.1016/j.chemolab.2014.09.015 10.1109/91.227387 10.1007/s12530-010-9006-x 10.1109/TFUZZ.2004.840099 10.1109/34.192473 10.1111/jfpe.13298 10.1021/jf970337t 10.1016/j.jhydrol.2019.06.045 10.1016/j.apm.2011.03.050 10.1111/jfpe.13085 10.1016/j.jpdc.2019.07.015 10.1016/j.neucom.2017.01.115 10.1016/j.cmpb.2018.03.020 10.1016/S0308-8146(96)00289-0 10.1016/j.neucom.2019.04.070 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier Ltd Copyright Elsevier BV Dec 1, 2021 |
| Copyright_xml | – notice: 2021 Elsevier Ltd – notice: Copyright Elsevier BV Dec 1, 2021 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.eswa.2021.115550 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology 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 Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-6793 |
| ExternalDocumentID | 10_1016_j_eswa_2021_115550 S0957417421009568 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ABYKQ ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW WUQ XPP ZMT ~HD 7SC 8FD AFXIZ AGCQF AGRNS BNPGV JQ2 L7M L~C L~D SSH |
| ID | FETCH-LOGICAL-c258t-ff700a68bd07bf3c50a24d5a16f39a08c543a773ef0a9a0eba24270b9b1d7b4a3 |
| ISICitedReferencesCount | 16 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000697030900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Fri Jul 25 07:42:50 EDT 2025 Sat Nov 29 07:05:05 EST 2025 Tue Nov 18 22:12:49 EST 2025 Fri Feb 23 02:42:51 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | PFCM clustering PFGG clustering Hyperellipsoidal data GG clustering Noise data |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c258t-ff700a68bd07bf3c50a24d5a16f39a08c543a773ef0a9a0eba24270b9b1d7b4a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2582220189 |
| PQPubID | 2045477 |
| ParticipantIDs | proquest_journals_2582220189 crossref_primary_10_1016_j_eswa_2021_115550 crossref_citationtrail_10_1016_j_eswa_2021_115550 elsevier_sciencedirect_doi_10_1016_j_eswa_2021_115550 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-01 2021-12-00 20211201 |
| PublicationDateYYYYMMDD | 2021-12-01 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2021 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Ji, Sun, Xia (b0075) 2011; 35 Pal, Pal, Keller, Bezdek (b0105) 2005; 13 Wu, Wu, Sun, Qiu, Li (b0140) 2015; 39 Al-Jowder, Kemsley, Wilson (b0080) 1997; 59 Beck, Duong, Lebbah, Azzag, Cérin (b0015) 2019; 134 Soleimani-B., Lucas, Araabi (b0125) 2010; 1 Małgorzata, Jerzy, Piotr, Piotr, Szymon, Sławomir (b0095) 2010; 69 Askari, Montazerin, Fazel Zarandi (b0010) 2017; 53 Ni, Luo, Zhu, Liu (b0100) 2019; 85 Liu, Liyanaarachchi Lekamalage, Huang, Lin (b0090) 2018; 277 Wu, Fu, Wu, Chen, Jia (b0135) 2020; 40 Wu, Wu, Sun, Zhao (b0150) 2011; 35 Yu, Lin, Tan (b0185) 2017; 121 Abonyi, Babuska, Szeifert (b0005) 2002; 32 Chen, Xu, Xia (b0020) 2013; 37 Wu, Zhou, Wu, Sun, Dai (b0155) 2019; 42 GeethaRamani, Lakshmi (b0070) 2018; 160 Sun, Zhou, Hu, Wu, Zhang, Wang (b0130) 2019; 160 Dai, Huang, Lv, Zhang, Sun, Aheto (b0035) 2018; 38 Yang, Qian, EL‐Mesery, Zhang, Wang, Tang (b0175) 2019; 99 Pedro, Thiago (b0110) 2017 Yin, Wang, Perakis (b0180) 2018; 48 Zhou, Sun, Tian, Wu, Dai, Li (b0195) 2019; 42 Wu, Zhu, Wu, Zhao, Sun, Dai (b0165) 2019; 8 Radhika, Rangarajan (b0115) 2019; 83 Sharma, Dey (b0120) 2019; 83–84 Chen, Wang, Zhang (b0025) 2019; 146 D’Urso, Massari (b0045) 2019; 505 Yan, Chen (b0170) 2009 Wu, Wu, Sun, Yang (b0145) 2017; 40 Dunn (b0050) 1973; 3 Banguero, Correcher, Pérez-Navarro, García, Aristizabal (b0055) 2020; 146 Downey, Briandet, Wilson, Kemsley (b0040) 1997; 45 Feng, Niu, Zhang, Wang, Cheng (b0060) 2019; 576 Krishnapuram, Keller (b0085) 1993; 1 Zheng, Fu, Ying (b0190) 2014; 139 Coletta, Ponti, Hruschka, Acharya, Ghosh (b0030) 2019; 358 Wu, Zhu, Wu, Sun, Dai (b0160) 2018; 147 Gath, Geva (b0065) 1989; 11 GeethaRamani (10.1016/j.eswa.2021.115550_b0070) 2018; 160 Wu (10.1016/j.eswa.2021.115550_b0155) 2019; 42 Zheng (10.1016/j.eswa.2021.115550_b0190) 2014; 139 Chen (10.1016/j.eswa.2021.115550_b0020) 2013; 37 Dunn (10.1016/j.eswa.2021.115550_b0050) 1973; 3 Wu (10.1016/j.eswa.2021.115550_b0135) 2020; 40 Wu (10.1016/j.eswa.2021.115550_b0165) 2019; 8 Gath (10.1016/j.eswa.2021.115550_b0065) 1989; 11 Pedro (10.1016/j.eswa.2021.115550_b0110) 2017 Sun (10.1016/j.eswa.2021.115550_b0130) 2019; 160 Wu (10.1016/j.eswa.2021.115550_b0160) 2018; 147 Radhika (10.1016/j.eswa.2021.115550_b0115) 2019; 83 Soleimani-B. (10.1016/j.eswa.2021.115550_b0125) 2010; 1 Krishnapuram (10.1016/j.eswa.2021.115550_b0085) 1993; 1 Abonyi (10.1016/j.eswa.2021.115550_b0005) 2002; 32 Wu (10.1016/j.eswa.2021.115550_b0150) 2011; 35 Yang (10.1016/j.eswa.2021.115550_b0175) 2019; 99 Yan (10.1016/j.eswa.2021.115550_b0170) 2009 D’Urso (10.1016/j.eswa.2021.115550_b0045) 2019; 505 Małgorzata (10.1016/j.eswa.2021.115550_b0095) 2010; 69 Yin (10.1016/j.eswa.2021.115550_b0180) 2018; 48 Banguero (10.1016/j.eswa.2021.115550_b0055) 2020; 146 Yu (10.1016/j.eswa.2021.115550_b0185) 2017; 121 Liu (10.1016/j.eswa.2021.115550_b0090) 2018; 277 Al-Jowder (10.1016/j.eswa.2021.115550_b0080) 1997; 59 Wu (10.1016/j.eswa.2021.115550_b0140) 2015; 39 Ni (10.1016/j.eswa.2021.115550_b0100) 2019; 85 Beck (10.1016/j.eswa.2021.115550_b0015) 2019; 134 Zhou (10.1016/j.eswa.2021.115550_b0195) 2019; 42 Askari (10.1016/j.eswa.2021.115550_b0010) 2017; 53 Chen (10.1016/j.eswa.2021.115550_b0025) 2019; 146 Wu (10.1016/j.eswa.2021.115550_b0145) 2017; 40 Ji (10.1016/j.eswa.2021.115550_b0075) 2011; 35 Pal (10.1016/j.eswa.2021.115550_b0105) 2005; 13 Dai (10.1016/j.eswa.2021.115550_b0035) 2018; 38 Sharma (10.1016/j.eswa.2021.115550_b0120) 2019; 83–84 Coletta (10.1016/j.eswa.2021.115550_b0030) 2019; 358 Feng (10.1016/j.eswa.2021.115550_b0060) 2019; 576 Downey (10.1016/j.eswa.2021.115550_b0040) 1997; 45 |
| References_xml | – volume: 35 start-page: 383 year: 2011 end-page: 397 ident: b0075 article-title: A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image publication-title: Computerized Medical Imaging and Graphics – year: 2017 ident: b0110 article-title: Proposal of new hybrid fuzzy clustering algorithms - Application to breast cancer dataset publication-title: 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE – volume: 69 start-page: 15 year: 2010 end-page: 24 ident: b0095 article-title: Complete gradient clustering algorithm for features analysis of X-Ray images publication-title: IEEE Transactions on Information Technology in Biomedicine – volume: 83–84 start-page: 1 year: 2019 end-page: 16 ident: b0120 article-title: Two-stage quality adaptive fingerprint image enhancement using fuzzy c-means clustering based fingerprint quality analysis publication-title: Image and Vision Computing – volume: 358 start-page: 150 year: 2019 end-page: 165 ident: b0030 article-title: Combining clustering and active learning for the detection and learning of new image classes publication-title: Neurocomputing – volume: 83 start-page: 105610 year: 2019 ident: b0115 article-title: On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction publication-title: Applied Soft Computing Journal – volume: 134 start-page: 128 year: 2019 end-page: 139 ident: b0015 article-title: A distributed approximate nearest neighbors algorithm for efficient large scale mean shift clustering publication-title: Journal of Parallel and Distributed Computing – volume: 1 start-page: 98 year: 1993 end-page: 110 ident: b0085 article-title: A possibilistic approach to clustering publication-title: IEEE Transactions on Fuzzy Systems – volume: 121 start-page: 33 year: 2017 end-page: 45 ident: b0185 article-title: A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering publication-title: Applied Acoustics – volume: 40 start-page: e12355 year: 2017 ident: b0145 article-title: Classification of apple varieties using near infrared reflectance spectroscopy and fuzzy discriminant c-means clustering model publication-title: Journal of Food Process Engineering – volume: 42 year: 2019 ident: b0155 article-title: Identification of tea varieties by mid-infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c-means clustering with a fuzzy covariance matrix publication-title: Journal of Food Process Engineering – volume: 11 start-page: 773 year: 1989 end-page: 781 ident: b0065 article-title: Unsupervised optimal fuzzy clustering publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 59 start-page: 195 year: 1997 end-page: 201 ident: b0080 article-title: Mid-infrared spectroscopy and authenticity problems in selected meats: A feasibility study publication-title: Food Chemistry – volume: 1 start-page: 59 year: 2010 end-page: 71 ident: b0125 article-title: Recursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modeling publication-title: Evolving Systems – volume: 147 start-page: 64 year: 2018 end-page: 69 ident: b0160 article-title: Discrimination of tea varieties using FTIR spectroscopy and allied Gustafson-Kessel clustering publication-title: Computers and Electronics in Agriculture – volume: 13 start-page: 517 year: 2005 end-page: 530 ident: b0105 article-title: A possibilistic fuzzy c-means clustering algorithm publication-title: IEEE Transactions on Fuzzy Systems – volume: 576 start-page: 229 year: 2019 end-page: 238 ident: b0060 article-title: Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization publication-title: Journal of Hydrology – volume: 53 start-page: 262 year: 2017 end-page: 283 ident: b0010 article-title: Generalized possibilistic fuzzy c-means with novel cluster validity indices for clustering noisy data publication-title: Applied Soft Computing Journal – volume: 139 start-page: 42 year: 2014 end-page: 47 ident: b0190 article-title: Spectroscopy-based food classification with extreme learning machine publication-title: Chemometrics and Intelligent Laboratory Systems – volume: 8 start-page: 38 year: 2019 ident: b0165 article-title: Discrimination of Chinese liquors based on electronic nose and fuzzy discriminant principal component analysis publication-title: Foods – year: 2009 ident: b0170 article-title: Hyperspherical possibilistic fuzzy c-means for high-dimensional data clustering publication-title: In – volume: 40 start-page: 512 year: 2020 end-page: 516 ident: b0135 article-title: Classification of FTNIR spectra of tea via possibilistic fuzzy discriminant C-means clustering publication-title: Spectroscopy and Spectral Analysis – volume: 146 start-page: 2438 year: 2020 end-page: 2449 ident: b0055 article-title: Diagnosis of a battery energy storage system based on principal component analysis publication-title: Renewable Energy – volume: 3 start-page: 32 year: 1973 end-page: 57 ident: b0050 article-title: A fuzzy relative of the ISODATA process and its use in detecting compact well separated cluster publication-title: Cybernet Systems – volume: 277 start-page: 78 year: 2018 end-page: 88 ident: b0090 article-title: Extreme learning machine for joint embedding and clustering publication-title: Neurocomputing – volume: 48 start-page: 2115 year: 2018 end-page: 2125 ident: b0180 article-title: A real-time sequential ship roll prediction scheme based on adaptive sliding data window publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 37 start-page: 2197 year: 2013 end-page: 2211 ident: b0020 article-title: Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis publication-title: Applied Mathematical Modelling – volume: 146 start-page: 41 year: 2019 end-page: 54 ident: b0025 article-title: A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting publication-title: Technological Forecasting & Social Change – volume: 505 start-page: 513 year: 2019 end-page: 534 ident: b0045 article-title: Fuzzy clustering of mixed data publication-title: Information Sciences – volume: 32 start-page: 612 year: 2002 end-page: 621 ident: b0005 article-title: Modified Gath-Geva fuzzy clustering for identification of takagi-sugeno fuzzy models publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B – volume: 160 start-page: 153 year: 2019 end-page: 159 ident: b0130 article-title: Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging publication-title: Computers and Electronics in Agriculture – volume: 42 year: 2019 ident: b0195 article-title: Spectral classification of lettuce cadmium stress based on information fusion and VISSA-GOA-SVM algorithm publication-title: Journal of Food Process Engineering – volume: 99 start-page: 6589 year: 2019 end-page: 6600 ident: b0175 article-title: Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree-confusion matrix method publication-title: Journal of the Science of Food and Agriculture – volume: 35 start-page: 4790 year: 2011 end-page: 4795 ident: b0150 article-title: Mixed fuzzy inter-cluster separation clustering algorithm publication-title: Applied Mathematical Modelling – volume: 160 start-page: 153 year: 2018 end-page: 163 ident: b0070 article-title: Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening publication-title: Computer Methods and Programs in Biomedicine – volume: 45 start-page: 4357 year: 1997 end-page: 4361 ident: b0040 article-title: Near-and mid-infrared spectroscopies in food authentication: Coffee varietal identification publication-title: Journal of Agricultural and Food Chemistry – volume: 85 start-page: 727 year: 2019 end-page: 739 ident: b0100 article-title: Clustering by finding prominent peaks in density space publication-title: Engineering Applications of Artificial Intelligence – volume: 39 start-page: 3398 year: 2015 end-page: 3409 ident: b0140 article-title: A hybrid fuzzy k-harmonic means clustering algorithm publication-title: Applied Mathematical Modelling – volume: 38 year: 2018 ident: b0035 article-title: Analysis of volatile compounds of publication-title: Journal of Food Safety – volume: 85 start-page: 727 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0100 article-title: Clustering by finding prominent peaks in density space publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2019.07.015 – volume: 38 issue: 6 year: 2018 ident: 10.1016/j.eswa.2021.115550_b0035 article-title: Analysis of volatile compounds of Tremella aurantialba fermentation via electronic nose and HS-SPME-GC-MS publication-title: Journal of Food Safety doi: 10.1111/jfs.12555 – volume: 39 start-page: 3398 issue: 12 year: 2015 ident: 10.1016/j.eswa.2021.115550_b0140 article-title: A hybrid fuzzy k-harmonic means clustering algorithm publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2014.11.041 – volume: 48 start-page: 2115 issue: 12 year: 2018 ident: 10.1016/j.eswa.2021.115550_b0180 article-title: A real-time sequential ship roll prediction scheme based on adaptive sliding data window publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems doi: 10.1109/TSMC.2017.2735995 – volume: 8 start-page: 38 issue: 1 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0165 article-title: Discrimination of Chinese liquors based on electronic nose and fuzzy discriminant principal component analysis publication-title: Foods doi: 10.3390/foods8010038 – volume: 37 start-page: 2197 issue: 4 year: 2013 ident: 10.1016/j.eswa.2021.115550_b0020 article-title: Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2012.04.031 – year: 2017 ident: 10.1016/j.eswa.2021.115550_b0110 article-title: Proposal of new hybrid fuzzy clustering algorithms - Application to breast cancer dataset – volume: 40 start-page: e12355 issue: 2 year: 2017 ident: 10.1016/j.eswa.2021.115550_b0145 article-title: Classification of apple varieties using near infrared reflectance spectroscopy and fuzzy discriminant c-means clustering model publication-title: Journal of Food Process Engineering doi: 10.1111/jfpe.12355 – year: 2009 ident: 10.1016/j.eswa.2021.115550_b0170 article-title: Hyperspherical possibilistic fuzzy c-means for high-dimensional data clustering – volume: 32 start-page: 612 issue: 5 year: 2002 ident: 10.1016/j.eswa.2021.115550_b0005 article-title: Modified Gath-Geva fuzzy clustering for identification of takagi-sugeno fuzzy models publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B doi: 10.1109/TSMCB.2002.1033180 – volume: 147 start-page: 64 year: 2018 ident: 10.1016/j.eswa.2021.115550_b0160 article-title: Discrimination of tea varieties using FTIR spectroscopy and allied Gustafson-Kessel clustering publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.02.014 – volume: 35 start-page: 383 issue: 5 year: 2011 ident: 10.1016/j.eswa.2021.115550_b0075 article-title: A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image publication-title: Computerized Medical Imaging and Graphics doi: 10.1016/j.compmedimag.2010.12.001 – volume: 160 start-page: 153 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0130 article-title: Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2019.03.004 – volume: 53 start-page: 262 year: 2017 ident: 10.1016/j.eswa.2021.115550_b0010 article-title: Generalized possibilistic fuzzy c-means with novel cluster validity indices for clustering noisy data publication-title: Applied Soft Computing Journal doi: 10.1016/j.asoc.2016.12.049 – volume: 83 start-page: 105610 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0115 article-title: On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction publication-title: Applied Soft Computing Journal doi: 10.1016/j.asoc.2019.105610 – volume: 146 start-page: 2438 year: 2020 ident: 10.1016/j.eswa.2021.115550_b0055 article-title: Diagnosis of a battery energy storage system based on principal component analysis publication-title: Renewable Energy doi: 10.1016/j.renene.2019.08.064 – volume: 121 start-page: 33 year: 2017 ident: 10.1016/j.eswa.2021.115550_b0185 article-title: A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering publication-title: Applied Acoustics doi: 10.1016/j.apacoust.2017.01.023 – volume: 69 start-page: 15 year: 2010 ident: 10.1016/j.eswa.2021.115550_b0095 article-title: Complete gradient clustering algorithm for features analysis of X-Ray images publication-title: IEEE Transactions on Information Technology in Biomedicine – volume: 83–84 start-page: 1 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0120 article-title: Two-stage quality adaptive fingerprint image enhancement using fuzzy c-means clustering based fingerprint quality analysis publication-title: Image and Vision Computing doi: 10.1016/j.imavis.2019.02.006 – volume: 3 start-page: 32 year: 1973 ident: 10.1016/j.eswa.2021.115550_b0050 article-title: A fuzzy relative of the ISODATA process and its use in detecting compact well separated cluster publication-title: Cybernet Systems – volume: 99 start-page: 6589 issue: 14 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0175 article-title: Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree-confusion matrix method publication-title: Journal of the Science of Food and Agriculture doi: 10.1002/jsfa.9943 – volume: 505 start-page: 513 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0045 article-title: Fuzzy clustering of mixed data publication-title: Information Sciences doi: 10.1016/j.ins.2019.07.100 – volume: 146 start-page: 41 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0025 article-title: A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting publication-title: Technological Forecasting & Social Change doi: 10.1016/j.techfore.2019.05.015 – volume: 139 start-page: 42 year: 2014 ident: 10.1016/j.eswa.2021.115550_b0190 article-title: Spectroscopy-based food classification with extreme learning machine publication-title: Chemometrics and Intelligent Laboratory Systems doi: 10.1016/j.chemolab.2014.09.015 – volume: 1 start-page: 98 issue: 2 year: 1993 ident: 10.1016/j.eswa.2021.115550_b0085 article-title: A possibilistic approach to clustering publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/91.227387 – volume: 1 start-page: 59 issue: 1 year: 2010 ident: 10.1016/j.eswa.2021.115550_b0125 article-title: Recursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modeling publication-title: Evolving Systems doi: 10.1007/s12530-010-9006-x – volume: 13 start-page: 517 issue: 4 year: 2005 ident: 10.1016/j.eswa.2021.115550_b0105 article-title: A possibilistic fuzzy c-means clustering algorithm publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2004.840099 – volume: 40 start-page: 512 issue: 2 year: 2020 ident: 10.1016/j.eswa.2021.115550_b0135 article-title: Classification of FTNIR spectra of tea via possibilistic fuzzy discriminant C-means clustering publication-title: Spectroscopy and Spectral Analysis – volume: 11 start-page: 773 issue: 7 year: 1989 ident: 10.1016/j.eswa.2021.115550_b0065 article-title: Unsupervised optimal fuzzy clustering publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/34.192473 – volume: 42 issue: 8 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0155 article-title: Identification of tea varieties by mid-infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c-means clustering with a fuzzy covariance matrix publication-title: Journal of Food Process Engineering doi: 10.1111/jfpe.13298 – volume: 45 start-page: 4357 year: 1997 ident: 10.1016/j.eswa.2021.115550_b0040 article-title: Near-and mid-infrared spectroscopies in food authentication: Coffee varietal identification publication-title: Journal of Agricultural and Food Chemistry doi: 10.1021/jf970337t – volume: 576 start-page: 229 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0060 article-title: Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2019.06.045 – volume: 35 start-page: 4790 issue: 10 year: 2011 ident: 10.1016/j.eswa.2021.115550_b0150 article-title: Mixed fuzzy inter-cluster separation clustering algorithm publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2011.03.050 – volume: 42 issue: 5 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0195 article-title: Spectral classification of lettuce cadmium stress based on information fusion and VISSA-GOA-SVM algorithm publication-title: Journal of Food Process Engineering doi: 10.1111/jfpe.13085 – volume: 134 start-page: 128 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0015 article-title: A distributed approximate nearest neighbors algorithm for efficient large scale mean shift clustering publication-title: Journal of Parallel and Distributed Computing doi: 10.1016/j.jpdc.2019.07.015 – volume: 277 start-page: 78 year: 2018 ident: 10.1016/j.eswa.2021.115550_b0090 article-title: Extreme learning machine for joint embedding and clustering publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.115 – volume: 160 start-page: 153 year: 2018 ident: 10.1016/j.eswa.2021.115550_b0070 article-title: Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2018.03.020 – volume: 59 start-page: 195 issue: 2 year: 1997 ident: 10.1016/j.eswa.2021.115550_b0080 article-title: Mid-infrared spectroscopy and authenticity problems in selected meats: A feasibility study publication-title: Food Chemistry doi: 10.1016/S0308-8146(96)00289-0 – volume: 358 start-page: 150 year: 2019 ident: 10.1016/j.eswa.2021.115550_b0030 article-title: Combining clustering and active learning for the detection and learning of new image classes publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.04.070 |
| SSID | ssj0017007 |
| Score | 2.4430737 |
| Snippet | [Display omitted]
•Propose PFGG clustering algorithm.•PFGG uses the exponential distance in contrast to PFCM.•PFGG clusters the dataset containing noisy data... As a famous clustering algorithm, Gath-Geva (GG) clustering has been widely applied in many fields. Unlike fuzzy c-means (FCM) clustering, GG clustering uses... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 115550 |
| SubjectTerms | Algorithms Clustering Covariance matrix Euclidean geometry GG clustering Hyperellipsoidal data Noise data Noise sensitivity PFCM clustering PFGG clustering |
| Title | A possibilistic fuzzy Gath-Geva clustering algorithm using the exponential distance |
| URI | https://dx.doi.org/10.1016/j.eswa.2021.115550 https://www.proquest.com/docview/2582220189 |
| Volume | 184 |
| WOSCitedRecordID | wos000697030900001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLeg48CFb8TGQD5wizLly7V9LGhsIDQhUaTeIjt1tlYjqZpkdPvreY7tJC3axA5cosSxX928X55fnt8HQh-UCIXMVe7HSSb9JJHC51xRX4YqmzPCuGBtoPA3enbGZjP-3W4XVG05AVoUbLPhq__KamgDZuvQ2XuwuyMKDXAOTIcjsB2O_8T4ibcqK-P0qnMwe3lzc3PtnYCm55-oKx0L2ejkCG1w4uV5uV7UF7-8pnJhU2qzKgvtQmR2b-oOFcvOaU-ta5sB2sXGDXbBOynfaO7NFqK8KO3q2Nqny7b9VJQbAOb5du-Pi6LvaA3ZU5hYrhZD60QUDjw9nJmR-kloKvH0EjcZyEzQSYlJPvuXODeWheWRqn7rHFFReNR33s6dvbOmdZ6GzoltmWoaqaaRGhoP0V5ECWcjtDf5cjz72u090cAE2buZ21Ar4xW4O5Pb1Jmdhb3VVqbP0BP7mYEnBh7P0QNVvEBPXQkPbCX6S_RjgrfQglu04A4tuEcL7tCCW7RgQAseoAU7tLxCPz8fTz-d-rbOhp9FhNV-nsNfFmMm5wGVeZyRQETJnIhwnMdcBCwjSSwojVUeCLhWEm5HNJBchnMqExG_RqMCfuwNwgwIZBEQTAjcEJEgPIEh4zGVPBSB2Eehe1xpZpPQ61ool-ntjNpHXjdmZVKw3NmbOC6kVok0ymEKoLpz3KFjWWrf5iqFxwP6cxAyfnCvSbxFj_uX4RCN6nWj3qFH2VW9qNbvLeD-APIeoPw |
| linkProvider | Elsevier |
| 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+possibilistic+fuzzy+Gath-Geva+clustering+algorithm+using+the+exponential+distance&rft.jtitle=Expert+systems+with+applications&rft.au=Wu%2C+Xiaohong&rft.au=Zhou%2C+Haoxiang&rft.au=Wu%2C+Bin&rft.au=Zhang%2C+Tingfei&rft.date=2021-12-01&rft.issn=0957-4174&rft.volume=184&rft.spage=115550&rft_id=info:doi/10.1016%2Fj.eswa.2021.115550&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2021_115550 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |