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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1729

Title: A hybrid algorithm for k-medoid clustering of large data sets
Authors: Liu, X
Sheng, W
Keywords: Biology computing
Computational complexity
Data structures
Genetic algorithms
Pattern clustering
Search problems
Very large databases
Publication Date: 2004
Publisher: IEEE
Citation: IEEE Evolutionary Computation, 2004. CEC Jun 2004
Abstract: In this paper, we propose a novel local search heuristic and then hybridize it with a genetic algorithm for k-medoid clustering of large data sets, which is an NP-hard optimization problem. The local search heuristic selects k-medoids from the data set and tries to efficiently minimize the total dissimilarity within each cluster. In order to deal with the local optimality, the local search heuristic is hybridized with a genetic algorithm and then the Hybrid K-medoid Algorithm (HKA) is proposed. Our experiments show that, compared with previous genetic algorithm based k-medoid clustering approaches - GCA and RAR/sub w/GA, HKA can provide better clustering solutions and do so more efficiently. Experiments use two gene expression data sets, which may involve large noise components.
URI: http://bura.brunel.ac.uk/handle/2438/1729
DOI: http://dx.doi.org/10.1109/CEC.2004.1330840
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Computer Science

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