Distributed Fair k-Center Clustering Problems with Outliers

Yuan, Fan; Diao, Luhong; Du, Donglei; Liu, Lei

doi:10.1007/978-3-030-96772-7_39

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Around the same time, (Chiplunkar et al, 2020) presented the previously discussed (3 + )-approximation two-pass streaming algorithm and a (17 + )-Mapreduce algorithm. (Yuan et al, 2021) study the fair k center problem with outliers and described a 4-approximation algorithm along with an 18-approximation distributed algorithm. Very recently, Angelidakis et al (Angelidakis et al, 2022) combined the fairness constraint with a privacy constraint and proposed a new model called the private and representative k-center where the privacy constraint means that every selected center has to cover at least a given amount of data.…”

Section: Definition Of the Fair K-center Problemmentioning

confidence: 99%

Fair $k$-Center: a Coreset Approach in Low Dimensions

Gan¹,

Golin²,

Yang³

et al. 2023

Preprint

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Center-based clustering techniques are fundamental in some areas of machine learning such as data summarization. Generic k-center algorithms can produce biased cluster representatives so there has been a recent interest in fair k-center clustering. Our main theoretical contributions are two new (3 + )-approximation algorithms for solving the fair k-center problem in (1) the dynamic incremental, i.e., one-pass streaming, model and (2) the MapReduce model. Our dynamic incremental algorithm is the first such algorithm for this problem (previous streaming algorithms required two passes) and our MapReduce one improves upon the previous approximation factor of (17 + ). Both algorithms work by maintaining a small coreset to represent the full point set and their analysis requires that the underlying metric has finite-doubling dimension. We also provide related heuristics for higher dimensional data and experimental results that compare the performance of our algorithms to existing ones.

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Section: Definition Of the Fair K-center Problemmentioning

confidence: 99%

Fair $k$-Center: a Coreset Approach in Low Dimensions

Gan¹,

Golin²,

Yang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…[21], which gave a MapReduce algorithm that requires constant rounds of information exchange, the approximation ratio is constant, and the algorithm is a sampling-based MapReduce algorithm, which is simple and easy to implement, and can be used to solve various clustering problems. In the conference version of this article [28] , we considered the distributed fair k-center clustering problem with outliers.…”

Section: Introductionmentioning

confidence: 99%

Fair $k$-Center Problem with Outliers on Massive Data

Yuan

Diao

et al. 2023

Tsinghua Sci. Technol.

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The clustering problem of big data in the era of artificial intelligence has been widely studied. Because of the huge amount of data, distributed algorithms are often used to deal with big data problems. The distributed computing model has an attractive feature: it can handle massive datasets that cannot be put into the main memory.On the other hand, since many decisions are made automatically by machines in today's society, algorithm fairness is also an important research area of machine learning. In this paper, we study two fair clustering problems: the centralized fair k-center problem with outliers and the distributed fair k-center problem with outliers. For these two problems, we have designed corresponding constant approximation ratio algorithms. The theoretical proof and analysis of the approximation ratio, and the running space of the algorithm are given.

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The Fair k-Center with Outliers Problem: FPT and Polynomial Approximations

Wu,

Feng,

et al. 2023

Lecture Notes in Computer Science

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Distributed Fair k-Center Clustering Problems with Outliers

Cited by 4 publications

References 16 publications

Fair $k$-Center: a Coreset Approach in Low Dimensions

Fair $k$-Center: a Coreset Approach in Low Dimensions

Fair $k$-Center Problem with Outliers on Massive Data

The Fair k-Center with Outliers Problem: FPT and Polynomial Approximations

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