Interpretable clustering using unsupervised binary trees

Ricardo Fraiman (), Badih Ghattas and Marcela Svarc ()

Advances in Data Analysis and Classification, 2013, vol. 7, issue 2, 125-145

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. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Unsupervised classification; CART; Pattern recognition; 62H30; 68T10 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11634-013-0129-3

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