Abstract
Cluster ensemble is a good alternative to face the problem of data clustering. Some studies based on mathematical models have shown that cluster ensemble methods lead to an effective improvement of the results of the standard clustering algorithms. In this paper, we focus on this problem, proposing a new approach to solve it, by adding a new step into the usual cluster ensemble methodology. Representing partitions by graphs and a new kernel function to measure the similarity between partitions are other proposals for this work. Experiments with synthetic and real databases show the suitability and effectiveness of our method.
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Vega-Pons, S., Correa-Morris, J., Ruiz-Shulcloper, J. (2008). Weighted Cluster Ensemble Using a Kernel Consensus Function. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_24
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DOI: https://doi.org/10.1007/978-3-540-85920-8_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85919-2
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