Abstract
Random partition models are widely used to perform clustering, since their features make them appealing options. However, additional information regarding group properties is not straightforward to incorporate under this approach. In order to overcome this difficulty, a novel approach to infer about clustering is presented. By relaxing the symmetry property of random partitions’ distributions, we are able to include group sizes in the computation of the probabilities. A Bayesian model is also given, together with a sampling scheme, and it is tested using simulated and real datasets.
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Acknowledgements
I would like to thank two anonymous referees for many helpful comments made on a previous version of the paper.
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Martínez, A.F. (2019). Clustering via Nonsymmetric Partition Distributions. In: Antoniano-Villalobos, I., Mena, R., Mendoza, M., Naranjo, L., Nieto-Barajas, L. (eds) Selected Contributions on Statistics and Data Science in Latin America. FNE 2018. Springer Proceedings in Mathematics & Statistics, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-31551-1_6
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DOI: https://doi.org/10.1007/978-3-030-31551-1_6
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