Abstract
Ensemble clustering generates data partitions by using different data representations and/or clustering algorithms. Each partition provides independent evidence to generate the final partition: two instances falling in the same cluster provide evidence towards them belonging to the same final partition.
In this paper we argue that, for some data representations, the fact that two instances fall in the same cluster of a given partition could provide little to no evidence towards them belonging to the same final partition. However, the fact that they fall in different clusters could provide strong negative evidence of them belonging to the same partition.
Based on this concept, we have developed a new ensemble clustering algorithm which has been applied to the heartbeat clustering problem. By taking advantage of the negative evidence we have decreased the misclassification rate over the MIT-BIH database, the gold standard test for this problem, from 2.25 % to 1.45 %.
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Acknowledgments
This work was supported by the University San Pablo CEU under the grant PPC12/2014. David G. Márquez is funded by an FPU Grant from the Spanish Ministry of Education (MEC) (Ref. AP2012-5053). Constantino A. García acknowledges the support of Xunta de Galicia under “Plan I2C” Grant program (partially cofunded by The European Social Fund of the European Union).
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Márquez, D.G., Fred, A.L.N., Otero, A., García, C.A., Félix, P. (2015). Introducing Negative Evidence in Ensemble Clustering Application in Automatic ECG Analysis. In: Feragen, A., Pelillo, M., Loog, M. (eds) Similarity-Based Pattern Recognition. SIMBAD 2015. Lecture Notes in Computer Science(), vol 9370. Springer, Cham. https://doi.org/10.1007/978-3-319-24261-3_5
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DOI: https://doi.org/10.1007/978-3-319-24261-3_5
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