American Institute of Mathematical Sciences

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April  2019, 15(2): 565-576. doi: 10.3934/jimo.2018057

An adaptive probabilistic algorithm for online k-center clustering

Ruiqi Yang 1, , Dachuan Xu 1, , Yicheng Xu 1, and  Dongmei Zhang 2,,

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

* Corresponding author: Dongmei Zhang

Received  August 2017 Revised  December 2017 Published  April 2018

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.

Keywords: k-center, online clustering, probabilistic algorithm.
Mathematics Subject Classification: Primary: 90C27; Secondary: 68W27.
Citation: Ruiqi Yang, Dachuan Xu, Yicheng Xu, Dongmei Zhang. An adaptive probabilistic algorithm for online k-center clustering. Journal of Industrial & Management Optimization, 2019, 15 (2) : 565-576. doi: 10.3934/jimo.2018057
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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

Figure 1.  An example of a sequence of points $1, x^2, ..., x^{2t}$ coming over time. The circles with dotted lines are the clusters produced by an online algorithm. Solid ones are those produced by the optimal off-line algorithm
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Figure 2.  An illustration of partition of $S^*_i$ for any given $i\in[k]$
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