Efficient clustering aggregation based on data fragments

Wu, O. and Hu, W. and Maybank, Stephen J. and Zhu, M. and Li, B. (2012) Efficient clustering aggregation based on data fragments. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42 (3), pp. 913-926. ISSN 1083-4419.

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Official URL: http://dx.doi.org/10.1109/TSMCB.2012.2183591

Abstract

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

Metadata

Item Type:	Article
Keyword(s) / Subject(s):	Clustering algorithms, Computational complexity, Correlation, Dispersion, Mutual information, Partitioning algorithms
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Administrator
Date Deposited:	06 Nov 2012 11:43
Last Modified:	09 Aug 2023 12:32
URI:	https://eprints.bbk.ac.uk/id/eprint/5570

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