Artículo
Interpretable clustering using unsupervised binary trees
Fecha de publicación:
03/2013
Editorial:
Springer
Revista:
Advances in Data Analysis and Classification
ISSN:
1862-5347
e-ISSN:
1862-5355
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first 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 (joining), 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.
Palabras clave:
Unsupervised Classification
,
Cart
,
Pattern Recognition
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Articulos de SEDE CENTRAL
Citación
Fraiman, Ricardo; Ghattas, Badih; Svarc, Marcela; Interpretable clustering using unsupervised binary trees; Springer; Advances in Data Analysis and Classification; 7; 2; 3-2013; 125-145
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