WebJan 1, 2024 · The scalable fair clustering algorithm is one of the variants of the disparate notion of fairness to the clustering problem. It solves this problem by ensuring that each cluster has an approximately equal number of points of each class and can accomplish this in nearly linear time. WebFeb 10, 2024 · The work most relevant to our paper is a recent manuscript by Schmidt et al. (), which proposed efficient streaming algorithms for fair k-means (which is similar to k-median studied here).Specifically, they give a near-linear time streaming algorithm for computing a core-set: a small subset S ⊆ P such that solving fair clustering over S yields …

Clustering with Fairness Constraints: A Flexible and Scalable …

WebThe performance of a scalable clustering algorithm for CPS’s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated. This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. WebJan 1, 2024 · The scalable fair clustering provides the solution to this limitation as an approximation algorithm to compute fairlet decomposition cost with running time in near … readers in rfid

KFC: A Scalable Approximation Algorithm for k-center Fair …

WebSCALABLE FAIR CLUSTERING. This is an implementation (written by the authors) of the fair k-median clustering algorithm from the paper: "Scalable Fair Clustering" by Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner. WebChierichetti et al. proposed a two-phase algorithm for fair k-clustering. In the first step, the pointset is partitioned into subsets called fairlets that satisfy the fairness requirement and approximately preserve the k-median objective. In the second step, fairlets are merged into k clusters by one of the existing k-median algorithms. how to store your guns