Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/7510
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dc.contributor.advisorLiu, X-
dc.contributor.authorLi, Jian-
dc.date.accessioned2013-07-03T15:28:29Z-
dc.date.available2013-07-03T15:28:29Z-
dc.date.issued2010-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/7510-
dc.descriptionThis thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel Universityen_US
dc.description.abstractTraditional clustering algorithms have different criteria and biases, and there is no single algorithm that can be the best solution for a wide range of data sets. This problem often presents a significant obstacle to analysts in revealing meaningful information buried among the huge amount of data. Ensemble Clustering has been proposed as a way to avoid the biases and improve the accuracy of clustering. The difficulty in developing Ensemble Clustering methods is to combine external information (provided by input clusterings) with internal information (i.e. characteristics of given data) effectively to improve the accuracy of clustering. The work presented in this thesis focuses on enhancing the clustering accuracy of Ensemble Clustering by employing heuristic optimisation techniques to achieve a robust combination of relevant information during the consensus clustering stage. Two novel heuristic optimisation-based Ensemble Clustering methods, Multi-Optimisation Consensus Clustering (MOCC) and K-Ants Consensus Clustering (KACC), are developed and introduced in this thesis. These methods utilise two heuristic optimisation algorithms (Simulated Annealing and Ant Colony Optimisation) for their Ensemble Clustering frameworks, and have been proved to outperform other methods in the area. The extensive experimental results, together with a detailed analysis, will be presented in this thesis.en_US
dc.language.isoenen_US
dc.publisherBrunel University, School of Information Systems, Computing and Mathematics-
dc.relation.ispartofSchool of Information Systems, Computing and Mathematics-
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/7510/1/FulltextThesis.pdf-
dc.subjectConsensus clusteringen_US
dc.subjectSimulated annealingen_US
dc.subjectAnt colony optimisationen_US
dc.subjectData miningen_US
dc.subjectCooling scheduleen_US
dc.titleEnsemble clustering via heuristic optimisationen_US
dc.typeThesisen_US
Appears in Collections:Computer Science
Dept of Computer Science Theses

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