Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
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

Rodrigues, Érick Oliveira, Torok, Leonardo, Liatsis, Panos, Viterbo, José, Conci, Aura (2017) k-MS: A novel clustering algorithm based on morphological reconstruction. Pattern Recognition, 66. 392-403 doi:10.1016/j.patcog.2016.12.027

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
Titlek-MS: A novel clustering algorithm based on morphological reconstruction
JournalPattern Recognition
AuthorsRodrigues, Érick OliveiraAuthor
Torok, LeonardoAuthor
Liatsis, PanosAuthor
Viterbo, JoséAuthor
Conci, AuraAuthor
Year2017 (June)Volume66
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2016.12.027Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9366906Long-form Identifiermindat:1:5:9366906:7
GUID0
Full ReferenceRodrigues, Érick Oliveira, Torok, Leonardo, Liatsis, Panos, Viterbo, José, Conci, Aura (2017) k-MS: A novel clustering algorithm based on morphological reconstruction. Pattern Recognition, 66. 392-403 doi:10.1016/j.patcog.2016.12.027
Plain TextRodrigues, Érick Oliveira, Torok, Leonardo, Liatsis, Panos, Viterbo, José, Conci, Aura (2017) k-MS: A novel clustering algorithm based on morphological reconstruction. Pattern Recognition, 66. 392-403 doi:10.1016/j.patcog.2016.12.027
In(2017) Pattern Recognition Vol. 66. Elsevier BV

See Also

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

Rodrigues, É.O., Conci, A., Liatsis, P. (2018) Morphological classifiers. Pattern Recognition, 84. 82-96 doi:10.1016/j.patcog.2018.06.010
Rodrigues, Érick Oliveira (2021) An efficient and locality-oriented Hausdorff distance algorithm: Proposal and analysis of paradigms and implementations. Pattern Recognition, 117. 107989pp. doi:10.1016/j.patcog.2021.107989
Wu, Yu, Mu, Tingting, Liatsis, Panos, Goulermas, John Y. (2017) Computation of heterogeneous object co-embeddings from relational measurements. Pattern Recognition, 65. 146-163 doi:10.1016/j.patcog.2016.12.004
Yujian, Li (2007) A clustering algorithm based on maximal -distant subtrees. Pattern Recognition, 40 (5). 1425-1431 doi:10.1016/j.patcog.2006.10.003
Bai, Liang, Cheng, Xueqi, Liang, Jiye, Shen, Huawei, Guo, Yike (2017) Fast density clustering strategies based on the k-means algorithm. Pattern Recognition, 71. 375-386 doi:10.1016/j.patcog.2017.06.023
Zhu, Wencheng, Lu, Jiwen, Zhou, Jie (2019) Structured general and specific multi-view subspace clustering. Pattern Recognition, 93. 392-403 doi:10.1016/j.patcog.2019.05.005
Li, Hao, Liu, Xiaojie, Li, Tao, Gan, Rundong (2020) A novel density-based clustering algorithm using nearest neighbor graph. Pattern Recognition, 102. 107206pp. doi:10.1016/j.patcog.2020.107206
Ismkhan, Hassan (2018) I-k-means−+: An iterative clustering algorithm based on an enhanced version of the k-means. Pattern Recognition, 79. 402-413 doi:10.1016/j.patcog.2018.02.015
Pérez-Suárez, Airel, Martínez-Trinidad, José Fco., Carrasco-Ochoa, Jesús A., Medina-Pagola, José E. (2013) An algorithm based on density and compactness for dynamic overlapping clustering. Pattern Recognition, 46 (11). 3040-3055 doi:10.1016/j.patcog.2013.03.022
Breger, Anna, Karner, Clemens, Ehler, Martin (2024) visClust: A visual clustering algorithm based on orthogonal projections. Pattern Recognition, 148. 110136 doi:10.1016/j.patcog.2023.110136
Rehman, Atiq Ur, Belhaouari, Samir Brahim (2022) Divide well to merge better: A novel clustering algorithm. Pattern Recognition, 122. 108305pp. doi:10.1016/j.patcog.2021.108305
 
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 © 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: August 18, 2025 11:07:31
Go to top of page