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RETRACTION: Peng Zhang, Chance-constrained multiperiod mean absolute deviation uncertain portfolio selection
An adaptive probabilistic algorithm for online k-center clustering
1. | Department of Information and Operations Research, College of Applied Sciences, Beijing University of Technology, Beijing 100124, China |
2. | School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China |
The $k$-center clustering is one of the well-studied clustering problems in computer science. We are given a set of data points $P\subseteq R^d$, where $R^d$ is $d$ dimensional Euclidean space. We need to select $k≤ |P|$ points as centers and partition the set $P$ into $k$ clusters with each point connecting to its nearest center. The goal is to minimize the maximum radius. We consider the so-called online $k$-center clustering model where the data points in $R^d$ arrive over time. We present the bi-criteria $(\frac{n}{k}, (\log\frac{U^*}{L^*})^2)$-competitive algorithm and $(\frac{n}{k}, \logγ\log\frac{nγ}{k})$-competitive algorithm for semi-online and fully-online $k$-center clustering respectively, where $U^*$ is the maximum cluster radius of optimal solution, $L^*$ is the minimum distance of two distinct points of $P$, $γ$ is the ratio of the maximum distance of two distinct points and the minimum distance of two distinct points of $P$ and $n$ is the number of points that will arrive in total.
References:
[1] |
D. Achlioptas, M. Chrobak and J. Noga,
Competitive analysis of randomized paging algorithms, Theoretical Computer Science, 234 (2000), 203-218.
doi: 10.1016/S0304-3975(98)00116-9. |
[2] |
J. Bar-Ilan, G. Kortsarz and D. Peleg,
How to allocate network centers, Journal of Algorithms, 15 (1993), 385-415.
doi: 10.1006/jagm.1993.1047. |
[3] |
M. Charikar, C. Chekuri, T. Feder and R. Motwani,
Incremental clustering and dynamic information retrieval, SIAM Journal on Computing, 33 (2004), 1417-1440.
doi: 10.1137/S0097539702418498. |
[4] |
S. Chaudhuri, N. Garg and R. Ravi,
The p-neighbor k-center problem, Information Processing Letters, 65 (1998), 131-134.
doi: 10.1016/S0020-0190(97)00224-X. |
[5] |
S. Chechik and D. Peleg,
The fault-tolerant capacitated k-center problem, Theoretical Computer Science, 566 (2015), 12-25.
doi: 10.1016/j.tcs.2014.11.017. |
[6] |
M. Cygan, M. T. Hajiaghayi and S. Khuller, LP rounding for k-centers with non-uniform hard capacities, Proceedings of the fifity-third Annual Symposium on Foundations of Computer Science (FOCS). IEEE, (2012), 273–282. |
[7] |
S. B. David, A. Borodin, R. Karp, G. Tardos and A. Wigderson,
On the power of randomization in on-line algorithms, Algorithmica, 11 (1994), 2-14.
doi: 10.1007/BF01294260. |
[8] |
T. Feder and D. Greene, Optimal algorithms for approximate clustering, Proceedings of the twentieth Annual ACM Symposium on Theory of Computing, ACM, (1988), 434–444.
doi: 10.1145/62212.62255. |
[9] |
C. G. Fernandes, S. P. de Paula and L. L. C. Pedrosa,
Improved approximation algorithms for capacitated fault-tolerant k-center, Algorithmica, 80 (2018), 1041-1072.
|
[10] |
T. F. Gonzalez,
Clustering to minimize the maximum intercluster distance, Theoretical Computer Science, 38 (1985), 293-306.
doi: 10.1016/0304-3975(85)90224-5. |
[11] |
D. S. Hochbaum and D. B. Shmoys,
A best possible heuristic for the k-center problem, Mathematics of Operations Research, 10 (1985), 180-184.
doi: 10.1287/moor.10.2.180. |
[12] |
W. L. Hsu and G. L. Nemhauser,
Easy and hard bottleneck location problems, Discrete Applied Mathematics, 1 (1979), 209-215.
doi: 10.1016/0166-218X(79)90044-1. |
[13] |
S. Khuller, R. Pless and Y. J. Sussmann,
Fault tolerant k-center problems, Theoretical Computer Science, 242 (2000), 237-245.
doi: 10.1016/S0304-3975(98)00222-9. |
[14] |
S. Khuller and Y. J. Sussmann,
The capacitated k-center problem, SIAM Journal on Discrete Mathematics, 13 (2000), 403-418.
doi: 10.1137/S0895480197329776. |
[15] |
S. O. Krumke,
On a generalization of the p-center problem, Information Processing Letters, 56 (1995), 67-71.
doi: 10.1016/0020-0190(95)00141-X. |
[16] |
E. Liberty, R. Sriharsha and M. Sviridenko, An algorithm for online k-means clustering, Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX), Society for Industrial and Applied Mathematics, (2016), 81–89.
doi: 10.1137/1.9781611974317.7. |
[17] |
R. G. Michael and S. J. David,
Computers and intractability: A guide to the theory of NP-completeness, W. H. Freeman, San Francisco, (1979), 90-91.
|
show all references
References:
[1] |
D. Achlioptas, M. Chrobak and J. Noga,
Competitive analysis of randomized paging algorithms, Theoretical Computer Science, 234 (2000), 203-218.
doi: 10.1016/S0304-3975(98)00116-9. |
[2] |
J. Bar-Ilan, G. Kortsarz and D. Peleg,
How to allocate network centers, Journal of Algorithms, 15 (1993), 385-415.
doi: 10.1006/jagm.1993.1047. |
[3] |
M. Charikar, C. Chekuri, T. Feder and R. Motwani,
Incremental clustering and dynamic information retrieval, SIAM Journal on Computing, 33 (2004), 1417-1440.
doi: 10.1137/S0097539702418498. |
[4] |
S. Chaudhuri, N. Garg and R. Ravi,
The p-neighbor k-center problem, Information Processing Letters, 65 (1998), 131-134.
doi: 10.1016/S0020-0190(97)00224-X. |
[5] |
S. Chechik and D. Peleg,
The fault-tolerant capacitated k-center problem, Theoretical Computer Science, 566 (2015), 12-25.
doi: 10.1016/j.tcs.2014.11.017. |
[6] |
M. Cygan, M. T. Hajiaghayi and S. Khuller, LP rounding for k-centers with non-uniform hard capacities, Proceedings of the fifity-third Annual Symposium on Foundations of Computer Science (FOCS). IEEE, (2012), 273–282. |
[7] |
S. B. David, A. Borodin, R. Karp, G. Tardos and A. Wigderson,
On the power of randomization in on-line algorithms, Algorithmica, 11 (1994), 2-14.
doi: 10.1007/BF01294260. |
[8] |
T. Feder and D. Greene, Optimal algorithms for approximate clustering, Proceedings of the twentieth Annual ACM Symposium on Theory of Computing, ACM, (1988), 434–444.
doi: 10.1145/62212.62255. |
[9] |
C. G. Fernandes, S. P. de Paula and L. L. C. Pedrosa,
Improved approximation algorithms for capacitated fault-tolerant k-center, Algorithmica, 80 (2018), 1041-1072.
|
[10] |
T. F. Gonzalez,
Clustering to minimize the maximum intercluster distance, Theoretical Computer Science, 38 (1985), 293-306.
doi: 10.1016/0304-3975(85)90224-5. |
[11] |
D. S. Hochbaum and D. B. Shmoys,
A best possible heuristic for the k-center problem, Mathematics of Operations Research, 10 (1985), 180-184.
doi: 10.1287/moor.10.2.180. |
[12] |
W. L. Hsu and G. L. Nemhauser,
Easy and hard bottleneck location problems, Discrete Applied Mathematics, 1 (1979), 209-215.
doi: 10.1016/0166-218X(79)90044-1. |
[13] |
S. Khuller, R. Pless and Y. J. Sussmann,
Fault tolerant k-center problems, Theoretical Computer Science, 242 (2000), 237-245.
doi: 10.1016/S0304-3975(98)00222-9. |
[14] |
S. Khuller and Y. J. Sussmann,
The capacitated k-center problem, SIAM Journal on Discrete Mathematics, 13 (2000), 403-418.
doi: 10.1137/S0895480197329776. |
[15] |
S. O. Krumke,
On a generalization of the p-center problem, Information Processing Letters, 56 (1995), 67-71.
doi: 10.1016/0020-0190(95)00141-X. |
[16] |
E. Liberty, R. Sriharsha and M. Sviridenko, An algorithm for online k-means clustering, Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX), Society for Industrial and Applied Mathematics, (2016), 81–89.
doi: 10.1137/1.9781611974317.7. |
[17] |
R. G. Michael and S. J. David,
Computers and intractability: A guide to the theory of NP-completeness, W. H. Freeman, San Francisco, (1979), 90-91.
|

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