A Pairwise Fair and Community-preserving Approach to k-Center Clustering
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Samir Khuller | Aravind Srinivasan | John P. Dickerson | Brian Brubach | Leonidas Tsepenekas | Darshan Chakrabarti | S. Khuller | A. Srinivasan | Brian Brubach | D. Chakrabarti | Leonidas Tsepenekas
[1] Piotr Indyk,et al. Similarity Search in High Dimensions via Hashing , 1999, VLDB.
[2] Rina Panigrahy,et al. Better streaming algorithms for clustering problems , 2003, STOC '03.
[3] Pierre Hansen,et al. Solving the p‐Center problem with Tabu Search and Variable Neighborhood Search , 2000, Networks.
[4] Andrew Lim,et al. k-Center problems with minimum coverage , 2004, Theor. Comput. Sci..
[5] N Linial,et al. Low diameter graph decompositions , 1993, Comb..
[6] Yair Bartal,et al. Probabilistic approximation of metric spaces and its algorithmic applications , 1996, Proceedings of 37th Conference on Foundations of Computer Science.
[7] Micah Altman,et al. The Promise and Perils of Computers in Redistricting , 2010 .
[8] John E. Beasley,et al. OR-Library: Distributing Test Problems by Electronic Mail , 1990 .
[9] Samir Khuller,et al. On the cost of essentially fair clusterings , 2018, APPROX-RANDOM.
[10] Sepideh Mahabadi,et al. (Individual) Fairness for k-Clustering , 2020, ICML.
[11] Satish Rao,et al. A tight bound on approximating arbitrary metrics by tree metrics , 2003, STOC '03.
[12] Piotr Indyk,et al. Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.
[13] Philip N. Klein,et al. Balanced centroidal power diagrams for redistricting , 2018, SIGSPATIAL/GIS.
[14] Toniann Pitassi,et al. Fairness through awareness , 2011, ITCS '12.
[15] Wolfgang Maass,et al. Approximation schemes for covering and packing problems in image processing and VLSI , 1985, JACM.
[16] Silvio Lattanzi,et al. Fair Clustering Through Fairlets , 2018, NIPS.
[17] Jurij Mihelic,et al. Solving the k-center Problem Efficiently with a Dominating Set Algorithm , 2005, J. Comput. Inf. Technol..
[18] Rajeev Motwani,et al. Incremental clustering and dynamic information retrieval , 1997, STOC '97.
[19] Aravind Srinivasan,et al. Approximation algorithms for stochastic clustering , 2018, NeurIPS.
[20] Oren Etzioni,et al. Fast and Intuitive Clustering of Web Documents , 1997, KDD.
[21] Samir Khuller,et al. The Capacitated K-Center Problem , 2000, SIAM J. Discret. Math..
[22] Christopher Jung,et al. Service in Your Neighborhood: Fairness in Center Location , 2020, FORC.
[23] Samir Khuller,et al. Streaming Algorithms for k-Center Clustering with Outliers and with Anonymity , 2008, APPROX-RANDOM.
[24] Shaowen Wang,et al. PEAR: a massively parallel evolutionary computation approach for political redistricting optimization and analysis , 2016, Swarm Evol. Comput..
[25] Samir Khuller,et al. Fault tolerant K-center problems , 1997, Theor. Comput. Sci..
[26] David B. Shmoys,et al. A Best Possible Heuristic for the k-Center Problem , 1985, Math. Oper. Res..
[27] Pranjal Awasthi,et al. Guarantees for Spectral Clustering with Fairness Constraints , 2019, ICML.
[28] Ravishankar Krishnaswamy,et al. The Non-Uniform k-Center Problem , 2016, ICALP.
[29] Michael Carl Tschantz,et al. Automated Experiments on Ad Privacy Settings , 2014, Proc. Priv. Enhancing Technol..
[30] Rong Ge,et al. Joint cluster analysis of attribute data and relationship data , 2008, SDM.
[31] Mikkel Thorup,et al. Quick k-Median, k-Center, and Facility Location for Sparse Graphs , 2001, SIAM J. Comput..
[32] Toniann Pitassi,et al. Learning Fair Representations , 2013, ICML.
[33] Gustavo Malkomes,et al. Fast Distributed k-Center Clustering with Outliers on Massive Data , 2015, NIPS.
[34] Aravind Srinivasan,et al. Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives , 2020, EC.
[35] Deeparnab Chakrabarty,et al. Fair Algorithms for Clustering , 2019, NeurIPS.
[36] Krzysztof Onak,et al. Scalable Fair Clustering , 2019, ICML.
[37] Itai Ashlagi,et al. Improving Community Cohesion in School Choice via Correlated-Lottery Implementation , 2014, Oper. Res..
[38] T L Chenevert,et al. Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies , 2010, American Journal of Neuroradiology.
[39] J. Beasley. A note on solving large p-median problems , 1985 .
[40] Micah Altman,et al. Modeling the effect of mandatory district compactness on partisan gerrymanders , 1998 .
[41] Teofilo F. GONZALEZ,et al. Clustering to Minimize the Maximum Intercluster Distance , 1985, Theor. Comput. Sci..
[42] David R. Karger,et al. Scatter/Gather: a cluster-based approach to browsing large document collections , 1992, SIGIR '92.
[43] Melanie Schmidt,et al. Privacy preserving clustering with constraints , 2018, ICALP.
[44] Nicole Immorlica,et al. Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.
[45] Ricardo Menchaca-Mendez,et al. When a worse approximation factor gives better performance: a 3-approximation algorithm for the vertex k-center problem , 2017, J. Heuristics.
[46] David B. Shmoys,et al. A unified approach to approximation algorithms for bottleneck problems , 1986, JACM.
[47] Pranjal Awasthi,et al. Fair k-Center Clustering for Data Summarization , 2019, ICML.
[48] Martine Labbé,et al. A New Formulation and Resolution Method for the p-Center Problem , 2004, INFORMS J. Comput..