Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A tabu search algorithm for cohesive clustering problems

  • Published:
Journal of Heuristics Aims and scope Submit manuscript

Abstract

Clustering problems can be found in a wide range of applications including data mining/analytics, logistics, healthcare, biotechnology, economic analysis and many other areas. Solving a clustering problem from the real world often poses significant challenges in spite of the fact that extensive research has been devoted to this topic. In this paper we present a tabu Search algorithm for a new problem class called cohesive clustering which arises in a variety of business applications. The class introduces an objective function to produce clusters as “pure” as possible, to maximize the similarity of the elements in each given cluster. Tabu search intensification and diversification strategies are employed in order to produce enhanced outcomes. The computational results demonstrate the effectiveness of the proposed algorithm.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • April, J., Better, M., Glover, F., Kelly, J.P., Kochenberger, G.: Strategic workforce optimization: ensuring workforce readiness with OptForce. Ann. Optim. (2014, in press)

  • Bae, E., Bailey, J., Dong, G.: A clustering comparison measure using density profiles and its application to the discovery of alternate clusterings. Data Min. Knowl. Discov. 21, 427–477 (2010)

    Article  MathSciNet  Google Scholar 

  • Brusco, M.J., Steinley, D., Cradit, J.D., Singh, R.: Emergent clustering methods for empirical OM research. J. Oper. Manag. 30, 454–466 (2012)

    Article  MATH  Google Scholar 

  • Cao, B., Glover, F.: Creating balanced and connected clusters for improved service delivery routes in logistics planning. J. Syst. Sci. Syst. Eng. 19, 453–480 (2010)

    Article  Google Scholar 

  • Datta, S., Giannella, C.R., Kargupta, H.: Approximate distributed K-means clustering over a peer-to-peer network. IEEE Trans. Knowl. Data Eng. 21(10), 1372–1388 (2009)

    Article  Google Scholar 

  • Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13, 533–549 (1986)

    Article  MathSciNet  Google Scholar 

  • Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)

    Book  MATH  Google Scholar 

  • Glover, F., Laguna, M.: Tabu search: effective strategies for hard problems in analytics and computational science. In: Pardalos, P.M., Du, D.-Z., Graham, R.L. (eds.) Handbook of Combinatorial Optimization, 2nd ed, vol. XXI, pp. 3261–3362. Springer, New York (2013)

  • Grabmeier, J., Rudolph, A.: Techniques of cluster algorithms in data mining. Data Min. Knowl. Discov. 6, 303–360 (2002)

    Article  MathSciNet  Google Scholar 

  • Kochenberger, Glover, F., Alidaee, B., Wang, H.: Clustering of microarray data via clique partitioning. J. Comb. Optim. 10, 77–92 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  • Kochenberger, G.A., Hao, J.K., Lü, Z., Wang, H., Glover, F.: Solving large scale Max Cut problems via tabu search. J. Heuristics 19(4), 565–571 (2013)

    Article  Google Scholar 

  • Linoff, G.S., Berry, M.J.: Data Mining Techniques, 3rd edn. Wiley Publishing Inc, Indianapolis, IN (2011)

    Google Scholar 

  • Liu, C.M.: Clustering techniques for stock location and order-picking in a distribution center. Comput. Oper. Res. 26, 989–1002 (1999)

    Article  MATH  Google Scholar 

  • Provost, F., Fawcett, T.: Data Science for Business. O’Reilly Media, Inc., Sebastopol, CA (2013)

    Google Scholar 

  • Strehl, A., Ghosh, J.: Relationship-based clustering and visualization for high-dimensional data mining. INFORMS J. Comput. 15, 1–23 (2002)

  • Trappey, C.V., Trappey, A.J.C., Chang, A.C., Huang, A.Y.L.: Clustering analysis prioritization of automobile logistics services. Ind. Manag. Data Syst. 110(5), 731–743 (2010)

    Article  Google Scholar 

  • Wu, Q., Hao, J.K., Glover, F.: Multi-neighborhood tabu search for the maximum weight clique problem. Ann. Oper. Res. 196(1), 611–634 (2013)

    Article  MathSciNet  Google Scholar 

  • Wu, H., Wang, X., Peng, Z., Li, Q.: Div-clustering: exploring active users for social collaborative recommendation. J. Netw. Comput. Appl. 36(6), 1642–1650 (2013)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank our student team including Aro Lee, Zheng Xu, and Jiayao Gao for their efforts in implementing the algorithm, data preparations, and partial computational experiments. We would also like to express our gratitude to two anonymous referees for their valuable criticisms and suggestions to improve our manuscript. This research is partially supported by project contract CIUC20140004.

Author information

Authors and Affiliations

  1. China Intelligent Urbanization Co-Creation Center for High Density Region, School of Software Engineering, Tongji University, 4800 Cao’An Road, Shanghai, 201804, China

    Buyang Cao

  2. OptTek Systems, Inc., 2241 17th Street, Boulder, CO, 80302, USA

    Fred Glover

  3. School of Business Administration, University of Mississippi, University, MS, 38677, USA

    Cesar Rego

Authors
  1. Buyang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fred Glover
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cesar Rego
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Buyang Cao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, B., Glover, F. & Rego, C. A tabu search algorithm for cohesive clustering problems. J Heuristics 21, 457–477 (2015). https://doi.org/10.1007/s10732-015-9285-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10732-015-9285-2

Keywords

Search

Navigation