Abstract
Clustering remains one of the most difficult challenges in data mining. This paper proposes a new algorithm, CLAM, using a hybrid metaheuristic between VNS and Tabu Search to solve the problem of k-medoid clustering. The new technique is compared to the well-known CLARANS. Experimental results show that, given the same computation times, CLAM is more effective.
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Blake, C., Newman, D.J., Hettich, S., Merz, C.: UCI Repository of Machine Learning Databases. University of California, Irvine (1998)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)
Deterding, D., Niranjan, M., Robinson, T.: UCI Repository of Machine Learning Databases. University of California, Irvine (1988)
Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 5, 533–549 (1986)
Glover, F., Laguna, M.: Tabu Search. Kluwer, Boston (1997)
Hansen, P., Mladenovic̀, N.: An introduction to variable neighborhood search. In: Voß, S., Martello, S., Osman, I.H., Roucairol, C. (eds.) Meta-heuristics: Advances and trends in local search paradigms for optimization. Proceedings of MIC 97 Conference. Kluwer Academic, Dordrecht (1998)
Hansen, P., Mladenovic, N.: Variable neighbourhood search. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 145–184. Kluwer Academic, Dordrecht (2003)
Kaufman, L.: Finding groups in data: an introduction to cluster analysis. In: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)
Ng, R.T., Han, J.: Efficient and effective clustering methods for spatial data mining. In: Proceedings of the Twentieth International Conference on Very Large Databases, pp. 144–155, Santiago, Chile (1994)
Ng, R.T., Han, J.: CLARANS: A method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14, 1003–1016 (2002)
Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs (1982)
Reeves, C.R.: Modern Heuristic Techniques for Combinatorial Problems. Wiley, New York (1993)
Wolberg, W.H., Mangasarian, O.: UCI Repository of Machine Learning Databases. University of California, Irvine (1992)
Yeh, I.-C.: UCI Repository of Machine Learning Databases. University of California, Irvine (2007)
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Nguyen, Q., Rayward-Smith, V.J. CLAM: Clustering Large Applications Using Metaheuristics. J Math Model Algor 10, 57–78 (2011). https://doi.org/10.1007/s10852-010-9141-1
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DOI: https://doi.org/10.1007/s10852-010-9141-1