2026· International Colloquium on Automata, Languages and Programming· pp. 112:1-112:21· 0 citations· 43 references
Computer Science
TL;DR
The techniques introduce novel dataset decompositions, enabling chaining arguments to be applied jointly across multiple components to solve the robust k -medians problem with m outliers.
This work develops a parameterized approximation algorithm for Euclidean fair k-center clustering with an approximation ratio of 4.732 and designs a one-pass streaming algorithm with an approximation ratio of 4.42, outperforming the state-of-the-art ratio.
Ze-Yu Lin, Chaoqi Jia, Long-Kun Guo et al.· Proceedings of the Thirty-Fi...· 0 citations
A generic reduction is given that transforms the (sensitive) input stream into a private stream, which is a semi-coreset of the input stream, which implies that any (non-private) online clustering algorithm, run as a post-processing step, can achieve good utility for the original clustering objective.
Edith Cohen, Vadym Doroshenko, Badih Ghazi et al.· 0 citations
Experiments show that KNNG-CS achieves accuracy comparable to representative gradient-approximation coreset methods, while reducing selection time by $2.3\times$-$41.2\times$ and peak memory to $0.3\%$-$7.5\%$ of the baselines.
Yingfan Liu, Lei-Yu Zhang, Jia-Dong Xie et al.· 1 citation
A randomized incremental ( 𝑘, 𝑧 ) -clustering algorithm that maintains with high probability a constant-factor approximation in a graph undergoing edge insertions with a total update time of ˜ 𝑂 ( 𝑘𝑚 1 + 𝑜 ( 1 ) + 𝑘 1 + 1 𝜆 𝑚 ) , where 𝜆 ≥ 1 is an arbitrary fixed constant.
Emilio Cruciani, Sebastian Forster, Antonis Skarlatos· 0 citations
Improved K-means method (MD-Kmeans) is proposed, which integrates K-nearest-neighbor– based density estimation with a maximum-dispersion strategy to ensure representative and well-distributed initial centers, and employs a balanced objective that jointly enhances intra-cluster compactness and inter-cluster separability...
Shaobo Deng, Yong-Kang Ye, Sujie Guan et al.· International Conference on...· 0 citations
K-SCAN is presented -- a novel hybrid algorithm that optimizes this trade-off between robustness to noise and ability to detect non-linear clusters while maintaining structural stability, and achieves more than a 3-fold speed-up over the hierarchical BIRCH algorithm.
F. Kosiorowski, Grzegorz Sroka· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
