Abstract
In this paper, we propose a cluster-based cumulative representation for cluster ensembles. Cluster labels are mapped to incrementally accumulated clusters, and a matching criterion based on maximum similarity is used. The ensemble method is investigated with bootstrap re-sampling, where the k-means algorithm is used to generate high granularity clusterings. For combining, group average hierarchical meta-clustering is applied and the Jaccard measure is used for cluster similarity computation. Patterns are assigned to combined meta-clusters based on estimated cluster assignment probabilities. The cluster-based cumulative ensembles are more compact than co-association-based ensembles. Experimental results on artificial and real data show reduction of the error rate across varying ensemble parameters and cluster structures.
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Ayad, H.G., Kamel, M.S. (2005). Cluster-Based Cumulative Ensembles. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_24
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DOI: https://doi.org/10.1007/11494683_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
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