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Found 6 papers, 0 papers with code
MRI Scan Synthesis Methods based on Clustering and Pix2Pix
The evaluation shows that the segmentation works well with synthesized scans (in particular, with Pix2Pix methods) in many cases.
Approximating Fair $k$-Min-Sum-Radii in $\mathbb{R}^d$
We propose a PTAS for the fair $k$-min-sum-radii problem in Euclidean spaces of arbitrary dimension for the case of constant $k$.
Coresets for constrained k-median and k-means clustering in low dimensional Euclidean space
There have been recent efforts to design unified algorithms to solve constrained $k$-means problems without using knowledge of the specific constraint at hand aside from mild assumptions like the polynomial computability of feasibility under the constraint (compute if a clustering satisfies the constraint) or the presence of an efficient assignment oracle (given a set of centers, produce an optimal assignment of points to the centers which satisfies the constraint).
Noisy, Greedy and Not So Greedy k-means++
We show that this is not the case by presenting a family of instances on which greedy k-means++ yields only an $\Omega(\ell\cdot \log k)$-approximation in expectation where $\ell$ is the number of possible centers that are sampled in each iteration.
Analysis of Ward's Method
In this paper, we show that Ward's method computes a $2$-approximation with respect to the $k$-means objective function if the optimal $k$-clustering is well separated.
Theoretical Analysis of the $k$-Means Algorithm - A Survey
The $k$-means algorithm is one of the most widely used clustering heuristics.
