Abstract
In this paper we present a method to compute dissimilarities on unlabeled data, based on extremely randomized trees. This method, Unsupervised Extremely Randomized Trees, is used jointly with a novel randomized labeling scheme we describe here, and that we call AddCl3. Unlike existing methods such as AddCl1 and AddCl2, no synthetic instances are generated, thus avoiding an increase in the size of the dataset. The empirical study of this method shows that Unsupervised Extremely Randomized Trees with AddCl3 provides competitive results regarding the quality of resulting clusterings, while clearly outperforming previous similar methods in terms of running time.
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Notes
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\(m_{try}\) is the number of variables used at each node when a tree is grown in RF.
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Acknowledgements
Kevin Dalleau’s PhD is funded by the RHU FIGHT-HF (ANR-15-RHUS-0004) and the Region Grand Est (France).
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Dalleau, K., Couceiro, M., Smail-Tabbone, M. (2018). Unsupervised Extremely Randomized Trees. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_38
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