Affordable Access

Clustering using Unsupervised Binary Trees: CUBT

Authors
  • Fraiman, Ricardo
  • Ghattas, Badih
  • Svarc, Marcela
Type
Preprint
Publication Date
Oct 27, 2011
Submission Date
Nov 11, 2010
Source
arXiv
License
Yellow
External links

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.

Report this publication

Statistics

Seen <100 times