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Explainable k-means. Don't be greedy, plant bigger trees!. (arXiv:2111.03193v2 [cs.LG] UPDATED)
April 28, 2022, 1:12 a.m. | Konstantin Makarychev, Liren Shan
cs.LG updates on arXiv.org arxiv.org
We provide a new bi-criteria $\tilde{O}(\log^2 k)$ competitive algorithm for
explainable $k$-means clustering. Explainable $k$-means was recently introduced
by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). It is described by
an easy to interpret and understand (threshold) decision tree or diagram. The
cost of the explainable $k$-means clustering equals to the sum of costs of its
clusters; and the cost of each cluster equals the sum of squared distances from
the points in the cluster to the center of that …
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