Interpretable Clustering using Unsupervised Binary Trees
| dc.creator.Autor | Fraiman, Ricardo | |
| dc.creator.Autor | Ghattas, Badih | |
| dc.creator.Autor | Svarc, Marcela | |
| dc.date.accessioned | 2012-07-06T14:16:57Z | |
| dc.date.available | 2012-07-06T14:16:57Z | |
| dc.date.issued | 2011 | |
| dc.description | Fil: Fraiman, Ricardo. Universidad de San Andrés. Departamento de Matemática y Ciencias; Argentina. | |
| dc.description | Fil: Ghattas, Badih. Universidad de San Andrés. Departamento de Matemática y Ciencias; Argentina. | |
| dc.description | Fil: Svarc, Marcela. Universidad de San Andrés. Departamento de Matemática y Ciencias; Argentina. | |
| dc.description.abstract | We herein introduce a new method of interpretable clustering that uses unsu- pervised binary trees. It is a three-stage procedure, the rst stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (join- ing), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets. | |
| dc.format | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10908/636 | |
| dc.language | eng | |
| dc.publisher | Universidad de San Andrés. Departamento de Matemáticas y Ciencias | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.title | Interpretable Clustering using Unsupervised Binary Trees | |
| dc.type | Documento de Trabajo | |
| dc.type | info:eu-repo/semantics/workingPaper | |
| dc.type | info:ar-repo/semantics/documento de trabajo | |
| dc.type | info:eu-repo/semantics/draft |