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2018

Interpretable Clustering via Optimal Trees

Bertsimas, Dimitris, Orfanoudaki, Agni, Wiberg, Holly

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State-of-the-art clustering algorithms use heuristics to partition the feature space and provide little insight into the rationale for cluster membership, limiting their interpretability.

In healthcare applications, the latter poses a barrier to the adoption of these methods since medical researchers are required to provide detailed explanations of their decisions in order to gain patient trust and limit liability.
We present a new unsupervised learning algorithm that leverages Mixed Integer Optimization techniques to generate interpretable tree-based clustering models.
Utilizing the flexible framework of Optimal Trees, our method approximates the globally optimal solution leading to high quality partitions of the feature space.

Interpretable Clustering via Optimal Trees · Around

Built on

“The Use and Interpretation of Principal Component Analysis in Applied Research”
C. Rao · 1964
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“Well-Separated Clusters and Optimal Fuzzy Partitions”
J Dunn · 1974
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“Classification and regression trees”
Leo Breiman, Jerome Friedman, Charles Stone and Richard Olshen · 1984
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“Silhouettes: a graphical aid to the interpretation and validation of cluster analysis”
Peter Rousseeuw · 1987
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“Centroid-based summarization of multiple documents”
Dragomir. Radev, Hongyan Jing, Małgorzata Styś and Daniel Tam · 2003
Earlier work this paper cites.

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Then

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