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Found 12 papers, 0 papers with code
Explainable k-means. Don't be greedy, plant bigger trees!
Our randomized bi-criteria algorithm constructs a threshold decision tree that partitions the data set into $(1+\delta)k$ clusters (where $\delta\in (0, 1)$ is a parameter of the algorithm).
Correlation Clustering with Asymmetric Classification Errors
In the Correlation Clustering problem, we are given a weighted graph $G$ with its edges labeled as "similar" or "dissimilar" by a binary classifier.
Local Correlation Clustering with Asymmetric Classification Errors
In the Correlation Clustering problem, we are given a complete weighted graph $G$ with its edges labeled as "similar" and "dissimilar" by a noisy binary classifier.
Near-optimal Algorithms for Explainable k-Medians and k-Means
This is an improvement over the previous guarantees of $O(k)$ and $O(k^2)$ by Dasgupta et al (2020).
Improved Guarantees for k-means++ and k-means++ Parallel
In this paper, we study k-means++ and k-means++ parallel, the two most popular algorithms for the classic k-means clustering problem.
Correlation clustering with local objectives
Classically, we are tasked with producing a clustering that minimizes the number of disagreements: an edge is in disagreement if it is a similar edge and is present across clusters or if it is a dissimilar edge and is present within a cluster.
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering
Further, the cost of every clustering is preserved within $(1+\varepsilon)$.
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions
We introduce and study the notion of an outer bi-Lipschitz extension of a map between Euclidean spaces.
Clustering Billions of Reads for DNA Data Storage
We provide empirical justification of the accuracy, scalability, and convergence of our algorithm on real and synthetic data.
Learning Communities in the Presence of Errors
Many algorithms exist for learning communities in the Stochastic Block Model, but they do not work well in the presence of errors.
Correlation Clustering with Noisy Partial Information
In this paper, we propose and study a semi-random model for the Correlation Clustering problem on arbitrary graphs G. We give two approximation algorithms for Correlation Clustering instances from this model.
Constant Factor Approximation for Balanced Cut in the PIE model
Let $G$ be an arbitrary graph on $V$ with no edges between $L$ and $R$.
