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Clustering without Over-Representation

Authors:

Alessandro Epasto,

Mohammad MahdianAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 267 - 275

https://doi.org/10.1145/3292500.3330987

Published: 25 July 2019 Publication History

Abstract

In this paper we consider clustering problems in which each point is endowed with a color. The goal is to cluster the points to minimize the classical clustering cost but with the additional constraint that no color is over-represented in any cluster. This problem is motivated by practical clustering settings, e.g., in clustering news articles where the color of an article is its source, it is preferable that no single news source dominates any cluster. For the most general version of this problem, we obtain an algorithm that has provable guarantees of performance; our algorithm is based on finding a fractional solution using a linear program and rounding the solution subsequently. For the special case of the problem where no color has an absolute majority in any cluster, we obtain a simpler combinatorial algorithm also with provable guarantees. Experiments on real-world data shows that our algorithms are effective in finding good clustering without over-representation.

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Cited By

Wu XFeng QHuang ZXu JWang JDastani MSichman JAlechina NDignum V(2024)New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential AlgorithmsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663057(1938-1946)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663057
Zhao BWang QTao ZFeng WGao QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)DFMVC: Deep Fair Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681099(8090-8099)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681099
Ceccarello MPietracaprina APucci GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Fast and Accurate Fair k-Center Clustering in Doubling MetricsProceedings of the ACM Web Conference 202410.1145/3589334.3645568(756-767)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645568
Show More Cited By

Index Terms

Clustering without Over-Representation
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Facility location and clustering
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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Information

Published In

cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2019

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KDD '19

Sponsor:

KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

Acceptance Rates

KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

Sponsor:
sigkdd
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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Wu XFeng QHuang ZXu JWang JDastani MSichman JAlechina NDignum V(2024)New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential AlgorithmsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663057(1938-1946)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663057
Zhao BWang QTao ZFeng WGao QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)DFMVC: Deep Fair Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681099(8090-8099)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681099
Ceccarello MPietracaprina APucci GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Fast and Accurate Fair k-Center Clustering in Doubling MetricsProceedings of the ACM Web Conference 202410.1145/3589334.3645568(756-767)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645568
Zhang YBao ZLi YZheng BWang X(2024)From a Timeline Contact Graph to Close Contact Tracing and Infection Diffusion InterventionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342347636:12(8328-8340)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3423476
Wu XFeng QXu JWang J(2024)New Algorithms for Fair k-Center Problem with Outliers and Capacity ConstraintsTheoretical Computer Science10.1016/j.tcs.2024.114515(114515)Online publication date: Mar-2024
https://doi.org/10.1016/j.tcs.2024.114515
Drexler LEube JLuo KReineccius DRöglin HSchmidt MWargalla J(2024)Connected k-Center and k-Diameter ClusteringAlgorithmica10.1007/s00453-024-01266-986:11(3425-3464)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1007/s00453-024-01266-9
Wickrama Arachchi CTatti N(2024)Fair Densest Subgraph Across Multiple GraphsMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70362-1_1(3-19)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70362-1_1
Lawless CGünlük O(2024)Fair Minimum Representation ClusteringIntegration of Constraint Programming, Artificial Intelligence, and Operations Research10.1007/978-3-031-60599-4_2(20-37)Online publication date: 25-May-2024
https://doi.org/10.1007/978-3-031-60599-4_2
Dickerson JEsmaeili SMorgenstern JZhang COh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Doubly constrained fair clusteringProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666704(13267-13293)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666704
Kar DKosan MMandal DMedya SSilva ADey PSanyal SAgmon NAn BRicci AYeoh W(2023)Feature-based Individual Fairness in k-clusteringProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599073(2772-2774)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3599073
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