CLAM: Clustering large applications using metaheuristics

Quynh Nguyen; V. J. Rayward-Smith

doi:10.1007/s10852-010-9141-1

CLAM: Clustering large applications using metaheuristics

Quynh Nguyen, V. J. Rayward-Smith

School of Computing Sciences

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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.

Original language	English
Pages (from-to)	57-78
Number of pages	22
Journal	Journal of Mathematical Modelling and Algorithms
Volume	10
Issue number	1
DOIs	https://doi.org/10.1007/s10852-010-9141-1
Publication status	Published - Mar 2011

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10.1007/s10852-010-9141-1

Cite this

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title = "CLAM: Clustering large applications using metaheuristics",

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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