<p><span><span><span><span><span><span><span>Advances in Data Analysis and Classification</span></span></span></span></span></span></span></p>

<article class="align-center media media--type-link media--view-mode-default"><div class="field field--name-field-media-entity-link field--type-link field--label-hidden field__item"><a href="https://link.springer.com/article/10.1007/s11634-013-0129-3" target="_blank">Vol. 7(2), pp. 125-145</a></div>
      
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Interpretable clustering using unsupervised binary trees

Interpretable clustering using unsupervised binary trees - https://link.springer.com/article/10.1007/s11634-013-0129-3

<strong>Abstract</strong>

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.

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Interpretable clustering using unsupervised binary tree

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