Explaining Kernel Clustering via Decision Trees

Published: 16 Jan 2024, Last Modified: 14 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Kernel k-means, Price of explainability
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Abstract: Despite the growing popularity of explainable and interpretable machine learning, there is still surprisingly limited work on inherently interpretable clustering methods. Recently, there has been a surge of interest in explaining the classic k-means algorithm, leading to efficient algorithms that approximate k-means clusters using axis-aligned decision trees. However, interpretable variants of k-means have limited applicability in practice, where more flexible clustering methods are often needed to obtain useful partitions of the data. In this work, we investigate interpretable kernel clustering, and propose algorithms that construct decision trees to approximate the partitions induced by kernel k-means, a nonlinear extension of k-means. We further build on previous work on explainable k-means and demonstrate how a suitable choice of features allows preserving interpretability without sacrificing approximation guarantees on the interpretable model.
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Primary Area: visualization or interpretation of learned representations
Submission Number: 7781