7 citations · 7 across the 4 of their papers we have counts for
4 papers
A Tight 3-Approximation for Fair -Means
Kangke Cheng, Guanlin Mo, Shihong Song +1
Fairness in clustering has attracted sustained research interest, motivated by the need to ensure equitable representation of protected groups in machine learning applications. We…
Sample-and-Search: An Effective Algorithm for Learning-Augmented k-Median Clustering in High dimensions
Kangke Cheng, Shihong Song, Guanlin Mo +1
In this paper, we investigate the learning-augmented -median clustering problem, which aims to improve the performance of traditional clustering algorithms by preprocessing the…
Relax and Merge: A Simple Yet Effective Framework for Solving Fair -Means and -sparse Wasserstein Barycenter Problems
Shihong Song, Guanlin Mo, Qingyuan Yang +1
The fairness of clustering algorithms has gained widespread attention across various areas, including machine learning, In this paper, we study fair -means clustering in Euclide…
Towards Metric DBSCAN: Exact, Approximate, and Streaming Algorithms
Guanlin Mo, Shihong Song, Hu Ding
DBSCAN is a popular density-based clustering algorithm that has many different applications in practice. However, the running time of DBSCAN in high-dimensional space or general me…