Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/7510
Title: Ensemble clustering via heuristic optimisation
Authors: Li, Jian
Advisors: Liu, X
Keywords: Consensus clustering;Simulated annealing;Ant colony optimisation;Data mining;Cooling schedule
Issue Date: 2010
Publisher: Brunel University, School of Information Systems, Computing and Mathematics
Abstract: Traditional 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.
Description: This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University
URI: http://bura.brunel.ac.uk/handle/2438/7510
Appears in Collections:Computer Science
Dept of Computer Science Theses

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