A data stream algorithm for the k-means problem which does not use Merge-and-Reduce and is named BICO as a combination of BIRCH and the term coreset, ...
Abstract: The k-means problem seeks a clustering that minimizes the sum of squared errors cost function: For input points P from the Euclidean space R^d and ...
Dec 28, 2018 · We show how to model and compute so-called coresets for fair clustering problems, which can be used to significantly reduce the input data size.
Missing: objectives. | Show results with:objectives.
Dec 17, 2014 · Page 1. Coresets and Streaming Algorithms for the k-means Problem and Related Clustering Objectives ... The k-means problem has been studied ...
People also ask
What is the objective of K clustering algorithm?
What are the goals of clustering algorithms?
This work proposes a variant of Lloyd's algorithm that computes fair clusterings and extend it to a fair k-means++ clustering algorithm and shows how to ...
We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points have a sensitive attribute and all clusters in the solution are ...
We study fair clustering problems as proposed by Chierichetti et al. Here, points have a sensitive attribute and all clusters in the solution are required ...
Coresets and streaming algorithms for the k-means problem and related
dblp.org › rec › books › daglib
Jul 17, 2021 · Bibliographic details on Coresets and streaming algorithms for the k-means problem and related clustering objectives.
Mar 9, 2021 · Abstract. We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points have a sensitive attribute and all ...
We need to define (dis)similarity! Page 7. Clustering. Examples of relevant distance and similarity measures. ○ Euclidean distance.