Interpretable clustering: an optimization approach
Name
10994_2020_5896_ReferencePDF.pdf
Size
1.42 MB
Format
Adobe PDF
Checksum (MD5)
e09ac80b76ad47a8afc0016b7e935a97
Author(s)
Bertsimas, Dimitris
Orfanoudaki, Agni
Wiberg, Holly
Date Issued
August 16, 2020
Publisher
Springer US
Version
Author's final manuscript
Abstract
Abstract
State-of-the-art clustering algorithms provide little insight into the rationale for cluster membership, limiting their interpretability. In complex real-world applications, the latter poses a barrier to machine learning adoption when experts are asked to provide detailed explanations of their algorithms’ recommendations. We present a new unsupervised learning method that leverages Mixed Integer Optimization techniques to generate interpretable tree-based clustering models. Utilizing a flexible optimization-driven framework, our algorithm approximates the globally optimal solution leading to high quality partitions of the feature space. We propose a novel method which can optimize for various clustering internal validation metrics and naturally determines the optimal number of clusters. It successfully addresses the challenge of mixed numerical and categorical data and achieves comparable or superior performance to other clustering methods on both synthetic and real-world datasets while offering significantly higher interpretability.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10994-020-05896-2
