Unsupervised Multiview Fuzzy C-Means Clustering Algorithm
The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm fo...
Uložené v:
| Vydané v: | Electronics (Basel) Ročník 12; číslo 21; s. 4467 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Basel
MDPI AG
01.11.2023
|
| Predmet: | |
| ISSN: | 2079-9292, 2079-9292 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended to process multiview datasets in the literature, called the multiview FCM (MV-FCM). However, most of the MV-FCM clustering algorithms and their extensions in the literature need prior information about the number of clusters and are also highly influenced by initializations. In this paper, we propose a novel MV-FCM clustering algorithm with an unsupervised learning framework, called the unsupervised MV-FCM (U-MV-FCM), such that it can search an optimal number of clusters during the iteration process of the algorithm without giving the number of clusters a priori. It is also free of initializations and parameter selection. We then use three synthetic and six benchmark datasets to make comparisons between the proposed U-MV-FCM and other existing algorithms and to highlight its practical implications. The experimental results show that our proposed U-MV-FCM algorithm is superior and more useful for clustering multiview datasets. |
|---|---|
| AbstractList | The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended to process multiview datasets in the literature, called the multiview FCM (MV-FCM). However, most of the MV-FCM clustering algorithms and their extensions in the literature need prior information about the number of clusters and are also highly influenced by initializations. In this paper, we propose a novel MV-FCM clustering algorithm with an unsupervised learning framework, called the unsupervised MV-FCM (U-MV-FCM), such that it can search an optimal number of clusters during the iteration process of the algorithm without giving the number of clusters a priori. It is also free of initializations and parameter selection. We then use three synthetic and six benchmark datasets to make comparisons between the proposed U-MV-FCM and other existing algorithms and to highlight its practical implications. The experimental results show that our proposed U-MV-FCM algorithm is superior and more useful for clustering multiview datasets. |
| Audience | Academic |
| Author | Hussain, Ishtiaq Sinaga, Kristina P. Yang, Miin-Shen |
| Author_xml | – sequence: 1 givenname: Ishtiaq orcidid: 0000-0003-2816-2397 surname: Hussain fullname: Hussain, Ishtiaq – sequence: 2 givenname: Kristina P. surname: Sinaga fullname: Sinaga, Kristina P. – sequence: 3 givenname: Miin-Shen orcidid: 0000-0002-4907-3548 surname: Yang fullname: Yang, Miin-Shen |
| BookMark | eNp9kN9LwzAQx4NMcM79Bb4UfO7Mj7ZpHkdxKmz44p5LTK81o0tmkk62v96M-SAi3sHdcdznvvC9RiNjDSB0S_CMMYHvoQcVnDVaeUIpybKCX6AxxVykggo6-jFfoan3GxxDEFYyPEZibfywA7fXHppkNfRB7zV8JovheDwkVboCaXxS9YMP4LTpknnfWafD-_YGXbay9zD97hO0Xjy8Vk_p8uXxuZovU8UKElKqMtKCpFLmLS_zBjPGM3Yqkog3gTOsBMtJGxe5yoBwXALOClmUkPOikWyC7s5_d85-DOBDvbGDM1GypmVZEiJwkcer2fmqkz3U2rQ2OKliNrDVKhrW6rifc05zFj0qIsDOgHLWewdtvXN6K92hJrg--Vr_4WukxC9K6SCDtibK6f5f9gsRzIIm |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2025_3562531 crossref_primary_10_3390_biomimetics10050331 crossref_primary_10_1155_mse_3092633 crossref_primary_10_1109_ACCESS_2025_3553252 crossref_primary_10_3390_bioengineering11121190 crossref_primary_10_3390_sym16121646 crossref_primary_10_1109_ACCESS_2024_3374208 crossref_primary_10_3390_app132212531 crossref_primary_10_1016_j_jclepro_2025_145054 crossref_primary_10_1088_1742_6596_2849_1_012120 crossref_primary_10_3390_fi17040150 crossref_primary_10_1016_j_neucom_2024_128884 crossref_primary_10_1016_j_ijtst_2024_10_001 crossref_primary_10_3390_electronics14142845 |
| Cites_doi | 10.1016/j.patcog.2017.12.014 10.1080/01969727308546047 10.1016/j.knosys.2020.105482 10.1016/0165-0114(78)90016-7 10.1109/TFUZZ.2020.3044253 10.1145/502512.502550 10.1109/ACCESS.2023.3243133 10.3390/electronics12010168 10.1109/TMM.2020.3019683 10.1109/TFUZZ.2017.2743679 10.2298/CSIS230130043P 10.1109/TFUZZ.2015.2421071 10.2307/2532201 10.1109/TCYB.2014.2334595 10.1109/ICDM.2009.138 10.1109/34.192473 10.1016/j.patcog.2017.05.017 10.1007/978-3-642-04180-8_45 10.1002/widm.1298 10.1007/s00500-012-0802-z 10.3390/s22187059 10.1016/S0167-8655(02)00130-7 10.3390/electronics9010188 10.1016/j.eswa.2016.10.006 10.1080/01621459.1983.10478008 10.1109/TFUZZ.2021.3058572 10.1007/BF02339490 10.18576/amis/100428 10.1016/j.patcog.2021.108064 10.1016/j.patcog.2009.02.010 10.1016/j.ins.2020.07.059 10.1080/01621459.1971.10482356 10.1016/S0019-9958(69)90591-9 10.1109/34.85677 10.1002/ece3.5774 10.1109/TPAMI.2013.142 10.3390/electronics9040554 10.1016/j.patcog.2019.107015 10.1109/34.927464 10.1080/01969727308546046 10.1109/TSMCB.2008.2004818 10.1109/ICDM.2016.0167 10.7554/eLife.12215 10.1016/S0019-9958(65)90241-X 10.1016/j.ins.2017.07.005 10.1109/TKDE.2019.2903810 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2023 MDPI AG 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2023 MDPI AG – notice: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 7SP 8FD 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L7M P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS |
| DOI | 10.3390/electronics12214467 |
| DatabaseName | CrossRef Electronics & Communications Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central Technology collection ProQuest One Community College ProQuest Central Korea SciTech Premium Collection Advanced Technologies Database with Aerospace ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China |
| DatabaseTitle | CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China 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 Advanced Technologies Database with Aerospace ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: PIMPY name: ProQuest Publicly Available Content url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2079-9292 |
| ExternalDocumentID | A772532146 10_3390_electronics12214467 |
| GeographicLocations | Taiwan |
| GeographicLocations_xml | – name: Taiwan |
| GroupedDBID | 5VS 8FE 8FG AAYXX ADMLS AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS ARAPS BENPR BGLVJ CCPQU CITATION HCIFZ IAO ITC KQ8 MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PROAC 7SP 8FD ABUWG AZQEC DWQXO L7M PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c361t-2c41fea2aa5f785d0337433374a19b9040c9351f3745c4e1708e046a68e576da3 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 20 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001170579300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2079-9292 |
| IngestDate | Fri Jul 25 07:04:50 EDT 2025 Tue Nov 04 18:30:44 EST 2025 Tue Nov 18 21:55:29 EST 2025 Sat Nov 29 07:16:08 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 21 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c361t-2c41fea2aa5f785d0337433374a19b9040c9351f3745c4e1708e046a68e576da3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-2816-2397 0000-0002-4907-3548 |
| OpenAccessLink | https://www.proquest.com/docview/2888119065?pq-origsite=%requestingapplication% |
| PQID | 2888119065 |
| PQPubID | 2032404 |
| ParticipantIDs | proquest_journals_2888119065 gale_infotracacademiconefile_A772532146 crossref_primary_10_3390_electronics12214467 crossref_citationtrail_10_3390_electronics12214467 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-11-01 |
| PublicationDateYYYYMMDD | 2023-11-01 |
| PublicationDate_xml | – month: 11 year: 2023 text: 2023-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Electronics (Basel) |
| PublicationYear | 2023 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Jaccard (ref_56) 1901; 37 Dunn (ref_12) 1975; 3 Jiang (ref_21) 2014; 45 Zeng (ref_23) 2017; 26 ref_55 ref_19 ref_18 ref_17 Chen (ref_26) 2020; 194 Wu (ref_42) 2009; 42 ref_15 Zadeh (ref_10) 1965; 8 Yang (ref_28) 2021; 119 Xu (ref_34) 2016; 10 Vidulin (ref_46) 2016; 44 Ruspini (ref_11) 1969; 15 Kumar (ref_16) 2020; 30 Rong (ref_52) 2021; 547 ref_20 Banfield (ref_4) 1993; 1 Lengyel (ref_32) 2019; 9 Yang (ref_29) 2023; 11 Tan (ref_27) 2020; 23 Chamroukhi (ref_7) 2019; 9 Yang (ref_33) 2016; 5 Wang (ref_48) 2019; 32 Huang (ref_25) 2020; 97 Zhong (ref_5) 2003; 4 Bezdek (ref_38) 1974; 1 ref_30 Roubens (ref_43) 1978; 1 Bezdek (ref_39) 1973; 3 Fowlkes (ref_54) 1983; 78 Chaomurilige (ref_13) 2015; 23 Gath (ref_40) 1989; 11 Yang (ref_35) 2017; 71 Zhu (ref_37) 2009; 39 Papakostas (ref_31) 2023; 20 Pereira (ref_44) 2013; 36 Benjamin (ref_24) 2022; 30 Hung (ref_2) 2012; 16 Lewis (ref_50) 2004; 5 ref_47 ref_45 Georghiades (ref_51) 2001; 23 Yu (ref_6) 2018; 77 Wang (ref_22) 2017; 72 Rand (ref_53) 1971; 66 ref_1 Xie (ref_41) 1991; 13 ref_3 ref_49 ref_9 ref_8 Chaomurilige (ref_14) 2017; 417 Pedrycz (ref_36) 2002; 23 |
| References_xml | – volume: 77 start-page: 188 year: 2018 ident: ref_6 article-title: On convergence and parameter selection of the EM and DA-EM algorithms for Gaussian mixtures publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.12.014 – volume: 3 start-page: 58 year: 1973 ident: ref_39 article-title: Cluster validity with fuzzy sets publication-title: J. Cybern. doi: 10.1080/01969727308546047 – volume: 194 start-page: 105482 year: 2020 ident: ref_26 article-title: Graph-regularized least squares regression for multi-view subspace clustering publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2020.105482 – volume: 1 start-page: 239 year: 1978 ident: ref_43 article-title: Pattern classification problems and fuzzy sets publication-title: Fuzzy Sets Syst. doi: 10.1016/0165-0114(78)90016-7 – ident: ref_55 – volume: 4 start-page: 1001 year: 2003 ident: ref_5 article-title: A unified framework for model-based clustering publication-title: J. Mach. Learn. Res. – volume: 30 start-page: 687 year: 2020 ident: ref_16 article-title: Bias-corrected intuitionistic fuzzy c-means with spatial neighborhood information approach for human brain MRI image segmentation publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2020.3044253 – ident: ref_18 doi: 10.1145/502512.502550 – volume: 11 start-page: 13574 year: 2023 ident: ref_29 article-title: Unsupervised multi-view K-means clustering algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3243133 – ident: ref_17 doi: 10.3390/electronics12010168 – volume: 23 start-page: 2943 year: 2020 ident: ref_27 article-title: Unsupervised multi-view clustering by squeezing hybrid knowledge from cross view and each view publication-title: IEEE Trans. Multimed. doi: 10.1109/TMM.2020.3019683 – volume: 26 start-page: 1671 year: 2017 ident: ref_23 article-title: A unified collaborative multikernel fuzzy clustering for multiview data publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2017.2743679 – volume: 20 start-page: 1389 year: 2023 ident: ref_31 article-title: PARSAT: Fuzzy logic for adaptive spatial ability training in an augmented reality system publication-title: Comput. Sci. Inf. Syst. doi: 10.2298/CSIS230130043P – ident: ref_1 – volume: 23 start-page: 2329 year: 2015 ident: ref_13 article-title: Analysis of parameter selection for Gustafson–Kessel fuzzy clustering using Jacobian matrix publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2015.2421071 – volume: 1 start-page: 803 year: 1993 ident: ref_4 article-title: Model-based Gaussian and non-Gaussian clustering publication-title: Biometrics doi: 10.2307/2532201 – ident: ref_8 – volume: 45 start-page: 688 year: 2014 ident: ref_21 article-title: Collaborative fuzzy clustering from multiple weighted views publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2014.2334595 – volume: 44 start-page: gkw964 year: 2016 ident: ref_46 article-title: The landscape of microbial phenotypic traits and associated genes publication-title: Nucleic Acids Res. – ident: ref_20 doi: 10.1109/ICDM.2009.138 – volume: 11 start-page: 773 year: 1989 ident: ref_40 article-title: Unsupervised optimal fuzzy clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.192473 – volume: 71 start-page: 45 year: 2017 ident: ref_35 article-title: Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.05.017 – ident: ref_49 doi: 10.1007/978-3-642-04180-8_45 – volume: 9 start-page: e1298 year: 2019 ident: ref_7 article-title: Model-based clustering and classification of functional data publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1298 – volume: 16 start-page: 1043 year: 2012 ident: ref_2 article-title: On mean shift-based clustering for circular data publication-title: Soft Comput. doi: 10.1007/s00500-012-0802-z – ident: ref_30 doi: 10.3390/s22187059 – volume: 23 start-page: 1675 year: 2002 ident: ref_36 article-title: Collaborative fuzzy clustering publication-title: Pattern Recognit. Lett. doi: 10.1016/S0167-8655(02)00130-7 – ident: ref_9 doi: 10.3390/electronics9010188 – volume: 72 start-page: 457 year: 2017 ident: ref_22 article-title: Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.10.006 – volume: 78 start-page: 553 year: 1983 ident: ref_54 article-title: A method for comparing two hierarchical clusterings publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1983.10478008 – volume: 37 start-page: 241 year: 1901 ident: ref_56 article-title: Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines publication-title: Bull. Soc. Vaudoise Sci. Nat. – volume: 30 start-page: 1357 year: 2022 ident: ref_24 article-title: Weighted multiview possibilistic c-means clustering with L2 regularization publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2021.3058572 – ident: ref_3 – volume: 1 start-page: 57 year: 1974 ident: ref_38 article-title: Numerical taxonomy with fuzzy sets publication-title: J. Math. Biol. doi: 10.1007/BF02339490 – volume: 10 start-page: 1493 year: 2016 ident: ref_34 article-title: Reviews on determining the number of clusters publication-title: Appl. Math. Inf. Sci. doi: 10.18576/amis/100428 – ident: ref_47 – volume: 119 start-page: 108064 year: 2021 ident: ref_28 article-title: Collaborative feature-weighted multi-view fuzzy c-means clustering publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2021.108064 – volume: 42 start-page: 2541 year: 2009 ident: ref_42 article-title: Robust cluster validity indexes publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2009.02.010 – volume: 547 start-page: 68 year: 2021 ident: ref_52 article-title: Learning a consensus affinity matrix for multi-view clustering via subspaces merging on Grassmann manifold publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.07.059 – volume: 66 start-page: 846 year: 1971 ident: ref_53 article-title: Objective criteria for the evaluation of clustering methods publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1971.10482356 – volume: 15 start-page: 22 year: 1969 ident: ref_11 article-title: A new approach to clustering publication-title: Inf. Control doi: 10.1016/S0019-9958(69)90591-9 – volume: 13 start-page: 841 year: 1991 ident: ref_41 article-title: validity measure for fuzzy clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.85677 – volume: 9 start-page: 13231 year: 2019 ident: ref_32 article-title: Silhouette width using generalized mean—A flexible method for assessing clustering efficiency publication-title: Ecol. Evol. doi: 10.1002/ece3.5774 – volume: 36 start-page: 521 year: 2013 ident: ref_44 article-title: On the role of correlation and abstraction in cross-modal multimedia retrieval publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2013.142 – ident: ref_15 doi: 10.3390/electronics9040554 – volume: 97 start-page: 107015 year: 2020 ident: ref_25 article-title: Auto-weighted multi-view clustering via deep matrix decomposition publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.107015 – volume: 23 start-page: 643 year: 2001 ident: ref_51 article-title: From few to many: Illumination cone models for face recognition under variable lighting and pose publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.927464 – volume: 3 start-page: 32 year: 1975 ident: ref_12 article-title: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters publication-title: J. Cybern. doi: 10.1080/01969727308546046 – volume: 39 start-page: 578 year: 2009 ident: ref_37 article-title: Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions publication-title: IEEE Trans. Syst. Man Cybern. Part B (Cybern.) doi: 10.1109/TSMCB.2008.2004818 – ident: ref_45 doi: 10.1109/ICDM.2016.0167 – volume: 5 start-page: e12215 year: 2016 ident: ref_33 article-title: Active sensing in the categorization of visual patterns publication-title: eLife doi: 10.7554/eLife.12215 – volume: 8 start-page: 338 year: 1965 ident: ref_10 article-title: Fuzzy sets publication-title: Inf. Control doi: 10.1016/S0019-9958(65)90241-X – ident: ref_19 – volume: 5 start-page: 361 year: 2004 ident: ref_50 article-title: Rcv1: A new benchmark collection for text categorization research publication-title: J. Mach. Learn. Res. – volume: 417 start-page: 435 year: 2017 ident: ref_14 article-title: Deterministic annealing Gustafson-Kessel fuzzy clustering algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.07.005 – volume: 32 start-page: 1116 year: 2019 ident: ref_48 article-title: GMC: Graph-based multi-view clustering publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2019.2903810 |
| SSID | ssj0000913830 |
| Score | 2.4076884 |
| Snippet | The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information.... |
| SourceID | proquest gale crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 4467 |
| SubjectTerms | Algorithms Analysis Augmented reality Clustering Clustering (Computers) Collaboration Datasets Electronic data processing Fuzzy sets Machine learning Memberships Methods Social networks Unsupervised learning |
| Title | Unsupervised Multiview Fuzzy C-Means Clustering Algorithm |
| URI | https://www.proquest.com/docview/2888119065 |
| Volume | 12 |
| WOSCitedRecordID | wos001170579300001&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: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2079-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913830 issn: 2079-9292 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2079-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913830 issn: 2079-9292 databaseCode: P5Z dateStart: 20120301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2079-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913830 issn: 2079-9292 databaseCode: BENPR dateStart: 20120301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content customDbUrl: eissn: 2079-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913830 issn: 2079-9292 databaseCode: PIMPY dateStart: 20120301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT-MwEB5B4bAcgH0gyks5IHFZi9iuHeeEStUKJFpFK0Cwl8hxHEAqbSEtEhz47YxTl4eEuHDxwYkdK-OZ8Uwy3wewK0JW8FDFRCqqCWqiIejXJeGZZTbkyoqKP-X8JOr11MVFnPiEW-l_q5zZxMpQ50PjcuT7DEM1it5LioPRHXGsUe7rqqfQmIcFh1TWqMHCYbuX_HvNsjjUS8XDKdwQx_h-_41dpqSswguLPrikzw1z5W06K99d5yos-3Nm0JxujJ8wZwe_YOkd-uBviM8G5WTkbEVp86CqxHVzB53J09Nj0CJdi24saPUnDksBRwTN_hU-aXx9-wfOOu3T1hHxTArEcEnHhJkGLaxmWosiUiIPOceTg2s0jbMYFdnEXNACO4RpWBqFymLgrCXKKpK55mtQGwwHdh0CWjDKZZ4VOQZXpsGzvBDKZNpIVrBMsTqw2ctMjYcZd2wX_RTDDSeB9BMJ1OHv66DRFGXj69v3nJRSp4M4t9G-lABX6NCs0iaGDMIxMMk6bM2klHrlLNM3EW18fXkTfjh2-Wnp4RbUxvcTuw2L5mF8U97v-L22A_Pd5za2ifiPfclxN7l8AQwF4uI |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3PT9swFH5iBWnsMGAbovzMAbQLFrFdO84BTVW3ioq26gEQnDLHcQZSKR1pQfBH8TfuOT-ASYgbBy45JLFl5315z8_J-z6AbeGzlPsqJFJRTfBNNATjuiQ8tsz6XFmR66ecdIN-X52ehoMZeKhqYdxvlZVPzB11cmXcHvkew1SNYvSS4sf4L3GqUe7raiWhUcDi0N7dYsqW7Xd-on13GGv_OmodkFJVgBgu6YQw06Cp1UxrkQZKJD7nGEXdQdMwDhHUJuSCpnhCmIalga8sJpFa4rgDmWiO_X6A2QaCXdVgdtDpDc4ed3Ucy6bifkFvxHno7z2p2WSU5fxkwX8h8OVAkEe39sJ7ey6L8LlcR3vNAvhLMGNHX-DTM3bFrxAej7Lp2PnCzCZeXmns5uK1p_f3d16L9CyGaa81nDquCGzhNYd_cGaT88tvcPwmY1-G2uhqZFfAoymjXCZxmmDyaBo8TlKhTKyNZCmLFasDq4wXmZJG3al5DCNMp5zFoxcsXofdx0bjgkXk9du_O1REzsdg30aXpRI4QsfWFTUxJRJOYUrWYb1CRVQ6nyx6gsTq65e34OPBUa8bdTv9wzWYZ7h-K8os16E2uZ7aDZgzN5OL7HqzxLkHv98aQv8AkS44-A |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LTxsxEB7xEqKHthQQaSndQ6teamVtx17voaqiQASCRjmUCvWyeL02IIUksEmr5Kf113W8DygS4saByx5215Zn5_OMx-v5BuCjCJnjoYqJVFQTnImGoF-XhKeW2ZArK4r6KT-Po15PnZ7G_QX4W-fC-GOVtU0sDHU2Mn6PvMkwVKPovaRouupYRH-v-218TXwFKf-ntS6nUULkyM7-YPiWfz3cQ11_Yqy7_6NzQKoKA8RwSSeEmRZ1VjOthYuUyELO0aP6i6ZxGiPATcwFdXhDmJalUagsBpRaogyRzDTHfhdhOcIY08-uvvh1u7_j-TYVD0uiI87jsHlX1yanrGAqi-45w4ddQuHnuq-e8xd6DS-r1XXQLqfDOizY4Rt48R_n4gbEJ8N8OvYWMrdZUOQfe7mC7nQ-nwUd8t2i8w46g6lnkMAWQXtwjpJNLq424eRJxr4FS8PR0G5DQB2jXGapyzCkNC2eZk4ok2ojmWOpYg1gtSITU5Gr-xofgwSDLK_95AHtN-DLbaNxyS3y-OufPUISb3mwb6OrBAocoefwStoYKAlfd0o2YKdGSFKZpDy5g8fbxx9_gFXETXJ82Dt6B2sMF3Vl7uUOLE1upvY9rJjfk8v8ZrcAfABnT42ff7t5QFs |
| 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=Unsupervised+Multiview+Fuzzy+C-Means+Clustering+Algorithm&rft.jtitle=Electronics+%28Basel%29&rft.au=Hussain%2C+Ishtiaq&rft.au=Sinaga%2C+Kristina+P.&rft.au=Yang%2C+Miin-Shen&rft.date=2023-11-01&rft.issn=2079-9292&rft.eissn=2079-9292&rft.volume=12&rft.issue=21&rft.spage=4467&rft_id=info:doi/10.3390%2Felectronics12214467&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_electronics12214467 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2079-9292&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2079-9292&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2079-9292&client=summon |