CLAM: Clustering large applications using metaheuristics

Quynh Nguyen, V. J. Rayward-Smith

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)57-78
Number of pages22
JournalJournal of Mathematical Modelling and Algorithms
Issue number1
Publication statusPublished - Mar 2011

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