A Global Optimization Algorithm for K-Center Clustering of One Billion Samples
References
- (2004) Method for targeted advertising on the web based on accumulated self-learning data, clustering users and semantic node graph techniques. US Patent 6714975.Google Scholar
- (2018) A sampling-based exact algorithm for the solution of the minimax diameter clustering problem. J. Global Optim. 71(3):613–630.Crossref, Google Scholar
- (1993) How to allocate network centers. J. Algorithms 15(3):385–415.Crossref, Google Scholar
- (2021) Extreme k-center clustering. Proc. AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 3941–3949.Google Scholar
- (2009) Parallel three dimensional direct simulation Monte Carlo for simulating micro flows. Parallel Computational Fluid Dynamics 2007, vol. 67 (Springer, Berlin), 91–98.Crossref, Google Scholar
- (2005) Branch-and-Bound Applications in Combinatorial Data Analysis (Springer, New York).Google Scholar
- (2021) MPI.jl: Julia bindings for the message passing interface. Proc. JuliaCon Conf. (JuliaCon) 1(1):68.Crossref, Google Scholar
- (2013) Exact solution methodologies for the P-center problem under single and multiple allocation strategies. Theses, Bilkent University, Turkey.Google Scholar
- (2019) A scalable global optimization algorithm for stochastic nonlinear programs. J. Global Optim. 75(2):393–416.Crossref, Google Scholar
- (2009) New relaxation-based algorithms for the optimal solution of the continuous and discrete p-center problems. Computers Oper. Res. 36(5):1646–1655.Crossref, Google Scholar
- (1987) Relaxation method for the solution of the minimax location-allocation problem in Euclidean space. Naval Res. Logist. 34(6):775–788.Crossref, Google Scholar
- (2020) How to solve fair k-center in massive data models. Proc. 37th Internat. Conf. Machine Learn. (PMLR, New York), 1877–1886.Google Scholar
- (2019) A scalable exact algorithm for the vertex p-center problem. Computers Oper. Res. 103(March):211–220.Crossref, Google Scholar
- Cook W, Lovasz L, Seymour P, eds. (1995) Combinatorial Optimization, DIMACS Series in Discrete Mathematics and Theoretical Computer Science (American Mathematical Society, Providence, RI).Crossref, Google Scholar
- (2020) V20.1.0: User’s Manual for CPLEX (International Business Machines Corporation, Armonk, NY).Google Scholar
- (2013) A declarative framework for constrained clustering. Joint Eur. Conf. Machine Learn. Knowledge Discovery Databases (Springer, Berlin, Heidelberg), 419–434.Crossref, Google Scholar
- (2017) Constrained clustering by constraint programming. Artificial Intelligence 244(March):70–94.Google Scholar
- (2000) A new approach to solving the vertex p-center problem to optimality: Algorithm and computational results. Comm. Oper. Res. Soc. Japan 45(9):428–436.Google Scholar
- (2024) Optimizing administrative divisions: A vertex k-center approach for edge-weighted road graphs. Baltic J. Modern Comput. 12(2):176–188.Crossref, Google Scholar
- (2011) Bee colony optimization for the p-center problem. Computers Oper. Res. 38(10):1367–1376.Crossref, Google Scholar
- (2017) UCI machine learning repository. Accessed October 17, 2022, http://archive.ics.uci.edu/ml.Google Scholar
- (1985) A simple heuristic for the p-centre problem. Oper. Res. Lett. 3(6):285–288.Crossref, Google Scholar
- (2004) A new formulation and resolution method for the p-center problem. INFORMS J. Comput. 16(1):84–94.Link, Google Scholar
- (2020) The parameterized hardness of the k-center problem in transportation networks. Algorithmica 82(7):1989–2005.Crossref, Google Scholar
- (2017) When a worse approximation factor gives better performance: A 3-approximation algorithm for the vertex k-center problem. J. Heuristics 23(5):349–366.Crossref, Google Scholar
- (2019) Approximation algorithms for the vertex K-center problem: Survey and experimental evaluation. IEEE Access 7(August):109228–109245.Crossref, Google Scholar
- (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness (W. H. Freeman, New York).Google Scholar
- (1985) Clustering to minimize the maximum intercluster distance. Theoretical Comput. Sci. 38:293–306.Crossref, Google Scholar
- (2009) Solving large p-median clustering problems by primal–dual variable neighborhood search. Data Mining Knowledge Discovery 19(3):351–375.Crossref, Google Scholar
- (2015) Data summarization techniques for big data—A survey. Handbook on Data Centers (Springer, New York), 1109–1152.Crossref, Google Scholar
- (1985) A best possible heuristic for the K-Center problem. Math. Oper. Res. 10(2):180–184.Link, Google Scholar
- (2013) Global Optimization: Deterministic Approaches (Springer Science & Business Media, Boston).Google Scholar
- (2021) A scalable deterministic global optimization algorithm for clustering problems. Proc. Internat. Conf. Machine Learn. (PMLR, New York), 4391–4401.Google Scholar
- (2001) An efficient exact algorithm for the vertex p-center problem. Accessed November 18, 2022, http://www.ie.bilkent.edu.tr/mustafap/pubs.Google Scholar
- (2022) Fair colorful k-center clustering. Math. Programming 192(July):339–360.Crossref, Google Scholar
- (2015) K-center: An approach on the multi-source identification of information diffusion. IEEE Trans. Inform. Forensics Security 10(12):2616–2626.Crossref, Google Scholar
- (2009) Finding Groups in Data: An Introduction to Cluster Analysis (John Wiley & Sons, New York).Google Scholar
- (2019) Fair k-center clustering for data summarization. Proc. Internat. Conf. Machine Learn. (PMLR, New York), 3448–3457.Google Scholar
- (2011) Covering models and optimization techniques for emergency response facility location and planning: A review. Math. Methods Oper. Res. 74(3):281–310.Crossref, Google Scholar
- (2005) K-center problems with minimum coverage. Theoretical Comput. Sci. 332(1):1–17.Crossref, Google Scholar
- (2015) Fast distributed k-center clustering with outliers on massive data. Proc. Internat. Conf. Neural Inform. Processing Systems (MIT Press, Cambridge, MA), 1063–1071.Google Scholar
- (2005) Solving the k-center problem efficiently with a dominating set algorithm. J. Comput. Inform. Tech. 13(3):225–234.Crossref, Google Scholar
- (1970) The m-center problem. SIAM Rev. 12(01):138–139.Crossref, Google Scholar
- (2003) Solving the p-center problem with Tabu search and variable neighborhood search. Networks 42(1):48–64.Crossref, Google Scholar
- (1991) A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems. SIAM Rev. 33(1):60–100.Crossref, Google Scholar
- (1987) A heuristic for the p-center problems in graphs. Discrete Appl. Math. 17(3):263–268.Crossref, Google Scholar
- (2016) Solving the k-centre problem as a method for supporting the Park and Ride facilities location decision. Proc. Federated Conf. Computer Sci. Inform. Systems (IEEE, Piscataway, NJ), 1223–1228.Google Scholar
- (2008) A memetic genetic algorithm for the vertex p-center problem. Evolutionary Comput. 16(3):417–436.Crossref, Google Scholar
- (2015) Analyzing 1.1 billion NYC taxi and uber trips with a vengeance. Accessed December 15, 2022, https://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/.Google Scholar
- (2022) Global optimization of k-center clustering. Proc. Internat. Conf. Machine Learn. (PMLR, New York), 19956–19966.Google Scholar
- (2022) Predicting xylose yield in prehydrolysis of hardwoods: A machine learning approach. Frontiers Chemical Engrg. 4(October):994428.Google Scholar
- (2019) Fault detection strategy based on weighted distance of k nearest neighbors for semiconductor manufacturing processes. IEEE Trans. Semiconductor Manufacturing 32(1):75–81. Crossref, Google Scholar

