# Distributed Partial Clustering

arXiv: https://arxiv.org/abs/1703.01539 · HTML: https://mlsanity.com/paper/1703.01539/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Fast Distributed k-Means with a Small Number of Rounds](https://mlsanity.com/paper/2201.13217/) (2022)
- [Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers](https://mlsanity.com/paper/2301.02814/) (2023)
- [Distributed k-Means with Outliers in General Metrics](https://mlsanity.com/paper/2202.08173/) (2022)

## Abstract (arXiv)

Recent years have witnessed an increasing popularity of algorithm design for distributed data, largely due to the fact that massive datasets are often collected and stored in different locations. In the distributed setting communication typically dominates the query processing time. Thus it becomes crucial to design communication efficient algorithms for queries on distributed data. Simultaneously, it has been widely recognized that partial optimizations, where we are allowed to disregard a small part of the data, provide us significantly better solutions. The motivation for disregarded points often arise from noise and other phenomena that are pervasive in large data scenarios. In this paper we focus on partial clustering problems, k-center, k-median and k-means, in the distributed model, and provide algorithms with communication sublinear of the input size. As a consequence we develop the first algorithms for the partial k-median and means objectives that run in subquadratic running time. We also initiate the study of distributed algorithms for clustering uncertain data, where each data point can possibly fall into multiple locations under certain probability distribution.