Clustering using Unsupervised Binary Trees: CUBT
Methodology
2011-10-28 v2 Machine Learning
Computation
Abstract
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.
Cite
@article{arxiv.1011.2624,
title = {Clustering using Unsupervised Binary Trees: CUBT},
author = {Ricardo Fraiman and Badih Ghattas and Marcela Svarc},
journal= {arXiv preprint arXiv:1011.2624},
year = {2011}
}
Comments
This paper has been withdrawn by the author due to an involuntary double submission to the arxiv