Vote for your favorite mineral in #MinCup25! - Chrysotile vs. Pectolite
Two new minerals to launch this year's cup! Hazardous (but useful!) chrysotile faces off against stunning gem pectolite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Berikov, Vladimir, Pestunov, Igor (2017) Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties. Pattern Recognition, 63. 427-436 doi:10.1016/j.patcog.2016.10.017

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleEnsemble clustering based on weighted co-association matrices: Error bound and convergence properties
JournalPattern Recognition
AuthorsBerikov, VladimirAuthor
Pestunov, IgorAuthor
Year2017 (March)Volume63
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2016.10.017Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9366817Long-form Identifiermindat:1:5:9366817:4
GUID0
Full ReferenceBerikov, Vladimir, Pestunov, Igor (2017) Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties. Pattern Recognition, 63. 427-436 doi:10.1016/j.patcog.2016.10.017
Plain TextBerikov, Vladimir, Pestunov, Igor (2017) Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties. Pattern Recognition, 63. 427-436 doi:10.1016/j.patcog.2016.10.017
In(2017) Pattern Recognition Vol. 63. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Zhang, Mimi (2022) Weighted clustering ensemble: A review. Pattern Recognition, 124. 108428pp. doi:10.1016/j.patcog.2021.108428
Yang, Yun, Jiang, Jianmin (2018) Bi-weighted ensemble via HMM-based approaches for temporal data clustering. Pattern Recognition, 76. 391-403 doi:10.1016/j.patcog.2017.11.022
Zhong, Caiming, Hu, Lianyu, Yue, Xiaodong, Luo, Ting, Fu, Qiang, Xu, Haiyong (2019) Ensemble clustering based on evidence extracted from the co-association matrix. Pattern Recognition, 92. 93-106 doi:10.1016/j.patcog.2019.03.020
Zhong, Caiming, Yue, Xiaodong, Zhang, Zehua, Lei, Jingsheng (2015) A clustering ensemble: Two-level-refined co-association matrix with path-based transformation. Pattern Recognition, 48 (8). 2699-2709 doi:10.1016/j.patcog.2015.02.014
Zhao, Xingwang, Liang, Jiye, Dang, Chuangyin (2017) Clustering ensemble selection for categorical data based on internal validity indices. Pattern Recognition, 69. 150-168 doi:10.1016/j.patcog.2017.04.019
Hu, Shizhe, Yan, Xiaoqiang, Ye, Yangdong (2020) Dynamic auto-weighted multi-view co-clustering. Pattern Recognition, 99. 107101pp. doi:10.1016/j.patcog.2019.107101
Sugahara, Kai, Okamoto, Kazushi (2023) Hierarchical co-clustering with augmented matrices from external domains. Pattern Recognition, 142. 109657 doi:10.1016/j.patcog.2023.109657
Wu, Ting, Hao, Yihang, Yang, Bo, Peng, Lizhi (2023) ECM-EFS: An ensemble feature selection based on enhanced co-association matrix. Pattern Recognition, 139. 109449 doi:10.1016/j.patcog.2023.109449
Qiu, Teng, Li, Yongjie (2021) Enhancing in-tree-based clustering via distance ensemble and kernelization. Pattern Recognition, 112. 107731pp. doi:10.1016/j.patcog.2020.107731
Melnykov, Volodymyr, Sarkar, Shuchismita, Melnykov, Yana (2021) On finite mixture modeling and model-based clustering of directed weighted multilayer networks. Pattern Recognition, 112. 107641pp. doi:10.1016/j.patcog.2020.107641
Lu, Zhoumin, Wang, Shiping, Liu, Genggeng, Nie, Feiping (2023) Robust weighted co-clustering with global and local discrimination. Pattern Recognition, 138. 109405 doi:10.1016/j.patcog.2023.109405
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 1, 2025 04:49:13
Go to top of page