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Impossibility of Depth Reduction in Explainable Clustering

https://doi.org/10.2139/ssrn.4724960
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11494669_93openalex-extracted
The Curse of Dimensionality in Data Mining and Time Series Prediction
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A Nearly Linear-Time Approximation Scheme for the Euclidean k-median Problem
inferredcites →https://doi.org/10.1007/978-3-662-04616-6_4openalex-extracted
Approximation Algorithms
inferredcites →https://doi.org/10.1016/0304-3975(85)90224-5openalex-extracted
Clustering to minimize the maximum intercluster distance
inferredcites →https://doi.org/10.1016/j.eswa.2016.06.009openalex-extracted
What makes classification trees comprehensible?
inferredcites →https://doi.org/10.1073/pnas.1900654116openalex-extracted
Definitions, methods, and applications in interpretable machine learning
inferredcites →https://doi.org/10.1109/focs.2016.46openalex-extracted
Local Search Yields Approximation Schemes for $k$-Means and $k$-Median in Euclidean and Minor-Free Metrics
inferredcites →https://doi.org/10.1109/focs.2016.47openalex-extracted
Local Search Yields a PTAS for $k$-Means in Doubling Metrics
inferredcites →https://doi.org/10.1109/focs.2019.00040openalex-extracted
Inapproximability of Clustering in Lp Metrics
inferredcites →https://doi.org/10.1109/focs57990.2023.00067openalex-extracted
The Price of Explainability for Clustering
inferredcites →https://doi.org/10.1137/1.9781611976465.156openalex-extracted
On Approximability of Clustering Problems Without Candidate Centers
inferredcites →https://doi.org/10.1137/1.9781611977073.103openalex-extracted
Almost Tight Approximation Algorithms for Explainable Clustering
inferredcites →https://doi.org/10.1137/18m1171321openalex-extracted
Better Guarantees for $k$-Means and Euclidean $k$-Median by Primal-Dual Algorithms
inferredcites →https://doi.org/10.1145/3477541openalex-extracted
Near-linear Time Approximation Schemes for Clustering in Doubling Metrics
inferredcites →https://doi.org/10.1145/3534678.3539361openalex-extracted
Optimal Interpretable Clustering Using Oblique Decision Trees
inferredcites →https://doi.org/10.1145/5925.5933openalex-extracted
A unified approach to approximation algorithms for bottleneck problems
inferredcites →https://doi.org/10.1145/62212.62255openalex-extracted
Optimal algorithms for approximate clustering
inferredcites →https://doi.org/10.1609/aaai.v36i4.20306openalex-extracted
How to Find a Good Explanation for Clustering?
inferredcites →https://doi.org/10.2139/ssrn.4218487openalex-extracted
The Computational Complexity of Some Explainable Clustering Problems
inferredcites →https://doi.org/10.48550/arxiv.1708.07381openalex-extracted
A Fast Approximation Scheme for Low-Dimensional $k$-Means
inferredcites →https://doi.org/10.48550/arxiv.2002.12538openalex-extracted
Explainable $k$-Means and $k$-Medians Clustering
inferredcites →https://doi.org/10.48550/arxiv.2006.02399openalex-extracted
ExKMC: Expanding Explainable $k$-Means Clustering
inferredcites →https://doi.org/10.48550/arxiv.2101.01576openalex-extracted
On the price of explainability for some clustering problems
inferredcites →https://doi.org/10.48550/arxiv.2106.15566openalex-extracted
Near-Optimal Explainable $k$-Means for All Dimensions
inferredcites →https://doi.org/10.48550/arxiv.2106.16147openalex-extracted
Nearly-Tight and Oblivious Algorithms for Explainable Clustering
inferredcites →https://doi.org/10.48550/arxiv.2107.00798openalex-extracted
Near-optimal Algorithms for Explainable k-Medians and k-Means
inferredcites →https://doi.org/10.48550/arxiv.2111.03193openalex-extracted
Explainable k-means. Don't be greedy, plant bigger trees!
inferredcites →https://doi.org/10.48550/arxiv.2111.10912openalex-extracted
Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in L_p metrics
inferredcites →https://doi.org/10.48550/arxiv.2112.14718openalex-extracted
Shallow decision trees for explainable $k$-means clustering
inferredcites →https://doi.org/10.48550/arxiv.2204.04828openalex-extracted
Improved Approximations for Euclidean $k$-means and $k$-median, via Nested Quasi-Independent Sets
inferredcites →https://doi.org/10.48550/arxiv.2210.08798openalex-extracted
Cluster Explanation via Polyhedral Descriptions
inferredcites →https://doi.org/10.52202/075280-2921openalex-extracted
Random Cuts are Optimal for Explainable k-Medians
inferredcites →https://doi.org/10.58248/pn633openalex-extracted
Interpretable machine learning
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