Automatically finding clusters in normalized cuts
Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very sensitive to initialization. Moreover, k-means tend...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_00313203_v44_n7_p1372_Tepper |
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todo:paper_00313203_v44_n7_p1372_Tepper2023-10-03T14:41:18Z Automatically finding clusters in normalized cuts Tepper, M. Musé, P. Almansa, A. Mejail, M. A contrario detection Clustering Normalized cuts A contrario detection Clustering Clustering methods Data dimensionality K-means Large clusters Normalized cuts Number of clusters Small clusters Spectral methods Spectral techniques Spectroscopy Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very sensitive to initialization. Moreover, k-means tends to split large clusters, to merge small clusters, and to favor convex-shaped clusters. In this work we present a new clustering method which is parameterless, independent from the original data dimensionality and from the shape of the clusters. It only takes into account inter-point distances and it has no random steps. The combination of the proposed method with normalized cuts proved successful in our experiments. © 2011 Elsevier Ltd. All rights reserved. Fil:Tepper, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00313203_v44_n7_p1372_Tepper |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
A contrario detection Clustering Normalized cuts A contrario detection Clustering Clustering methods Data dimensionality K-means Large clusters Normalized cuts Number of clusters Small clusters Spectral methods Spectral techniques Spectroscopy |
| spellingShingle |
A contrario detection Clustering Normalized cuts A contrario detection Clustering Clustering methods Data dimensionality K-means Large clusters Normalized cuts Number of clusters Small clusters Spectral methods Spectral techniques Spectroscopy Tepper, M. Musé, P. Almansa, A. Mejail, M. Automatically finding clusters in normalized cuts |
| topic_facet |
A contrario detection Clustering Normalized cuts A contrario detection Clustering Clustering methods Data dimensionality K-means Large clusters Normalized cuts Number of clusters Small clusters Spectral methods Spectral techniques Spectroscopy |
| description |
Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very sensitive to initialization. Moreover, k-means tends to split large clusters, to merge small clusters, and to favor convex-shaped clusters. In this work we present a new clustering method which is parameterless, independent from the original data dimensionality and from the shape of the clusters. It only takes into account inter-point distances and it has no random steps. The combination of the proposed method with normalized cuts proved successful in our experiments. © 2011 Elsevier Ltd. All rights reserved. |
| format |
JOUR |
| author |
Tepper, M. Musé, P. Almansa, A. Mejail, M. |
| author_facet |
Tepper, M. Musé, P. Almansa, A. Mejail, M. |
| author_sort |
Tepper, M. |
| title |
Automatically finding clusters in normalized cuts |
| title_short |
Automatically finding clusters in normalized cuts |
| title_full |
Automatically finding clusters in normalized cuts |
| title_fullStr |
Automatically finding clusters in normalized cuts |
| title_full_unstemmed |
Automatically finding clusters in normalized cuts |
| title_sort |
automatically finding clusters in normalized cuts |
| url |
http://hdl.handle.net/20.500.12110/paper_00313203_v44_n7_p1372_Tepper |
| work_keys_str_mv |
AT tepperm automaticallyfindingclustersinnormalizedcuts AT musep automaticallyfindingclustersinnormalizedcuts AT almansaa automaticallyfindingclustersinnormalizedcuts AT mejailm automaticallyfindingclustersinnormalizedcuts |
| _version_ |
1782025397729230848 |