Skip to main content

Local Search for K-medians and Facility Location

2001; Arya, Garg, Khandekar, Meyerson, Munagala, Pandit

  • Reference work entry
Encyclopedia of Algorithms
  • 238 Accesses

Keywords and Synonyms

k-Medians ; k-Means; k-Medioids; Facility location; Point location; Warehouse location; Clustering          

Problem Definition

Clustering is a form of unsupervised learning, where the goal is to “learn” useful patterns in a data set \( { \mathcal{D} } \) of size n. It can also be thought of as a data compression scheme where a large data set is represented using a smaller collection of “representatives”. Such a scheme is characterized by specifying the following:

  1. 1.

    distance metric \( { \mathbf{d} } \) between items in the data set. This metric should satisfy the triangle inequality: \( { \mathbf{d}(i,j) \le \mathbf{d}(j,k) + \mathbf{d}(k,i) } \) for any three items \( { i,j,k \in \mathcal{D} } \). In addition, \( { \mathbf{d}(i,j) = \mathbf{d}(j,i) } \) for all \( { i,j \in \mathcal{S} } \) and \( { \mathbf{d}(i,i) = 0 } \). Intuitively, if the distance between two items is smaller, they are more similar. The items are usually points in some high dimensional Euclidean...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 399.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Arya, V., Garg, N., Khandekar, R., Meyerson, A., Munagala, K., Pandit, V.: Local search heuristics for k-median and facility location problems. SIAM J. Comput. 33(3), 544–562 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  2. Charikar, M., Guha, S.: Improved combinatorial algorithms for facility location problems. SIAM J. Comput. 34(4), 803–824 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  3. Charikar, M., Guha, S., Tardos, É., Shmoys, D.B.: A constant-factor approximation algorithm for the k-median problem (extended abstract). In: STOC '99: Proceedings of the thirty-first annual ACM symposium on Theory of computing, pp. 1–10. Atlanta, May 1-4 1999

    Google Scholar 

  4. Chudak, F.A., Williamson, D.P.: Improved approximation algorithms for capacitated facility location problems. Math. Program. 102(2), 207–222 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  5. Cornuejols, G., Nemhauser, G.L., Wolsey, L.A.: The uncapacitated facility location problem. In: Discrete Location Theory, pp. 119–171. Wiley, New York (1990)

    Google Scholar 

  6. Jain, K., Mahdian, M., Markakis, E., Saberi, A., Vazirani, V.V.: Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP. J. ACM 50(6), 795–824 (2003)

    Article  MathSciNet  Google Scholar 

  7. Jain, K., Vazirani, V.V.: Approximation algorithms for metric facility location and k-median problems using the primal-dual schema and lagrangian relaxation. J. ACM 48(2), 274–296 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  8. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)

    Book  Google Scholar 

  9. Korupolu, M.R., Plaxton, C.G., Rajaraman, R.: Analysis of a local search heuristic for facility location problems. In: SODA '98: Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms, pp. 1–10. San Francisco, USA; 25–26 January 1998

    Google Scholar 

  10. Kuehn, A.A., Hamburger, M.J.: A heuristic program for locating warehouses. Management Sci. 9(4), 643–666 (1963)

    Article  Google Scholar 

  11. Lin, J.-H., Vitter, J.S.: ε-approximations with minimum packing constraint violation (extended abstract). In: STOC '92: Proceedings of the twenty-fourth annual ACM symposium on Theory of computing, pp. 771–782. Victoria (1992)

    Google Scholar 

  12. Mahdian, M., Pál, M.: Universal facility location. In: European Symposium on Algorithms, pp. 409–421. Budapest, Hungary, September 16–19 2003

    Google Scholar 

  13. Ng, R.T., Han, J.: Efficient and effective clustering methods for spatial data mining. In: Proc. Symp. on Very Large Data Bases (VLDB), pp. 144–155. Santiago de Chile, 12–15 September 1994

    Google Scholar 

  14. Pál, M., Tardos, É., Wexler, T.: Facility location with nonuniform hard capacities. In: Proceedings of the 42nd Annual Symposium on Foundations of Computer Science, pp. 329–338. Las Vegas, 14–17 October 2001

    Google Scholar 

  15. Shmoys, D.B., Tardos, É., and Aardal, K.: Approximation algorithms for facility location problems. In: Proceedings of the Twenty-Ninth Annual ACM Symposium on Theory of Computing, pp. 265–274. El Paso, 4–6 May 1997

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag

About this entry

Cite this entry

Munagala, K. (2008). Local Search for K-medians and Facility Location. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30162-4_212

Download citation

Publish with us

Policies and ethics