Abstract
The choice of the number of clusters is a leading problem in Machine Learning. Validation methods provide solutions, with the drawback that inference is not possible. In this manuscript, we derive a distribution for the number of clusters for clustering validation. The starting point of our approach is the data transformation to the probabilistic space. Then, the dependence of the non-negative factorization to the dimensionality of the space span provides a sequence of the traces when the dimensionality varies. Its limit is a gamma. This result allows a non-excluding discussion when interpreting probabilities as credibility levels, and we open the door to inference for clustering validation.
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Figuera, P., Cuzzocrea, A., García Bringas, P. (2023). Probability Density Function for Clustering Validation. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_12
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DOI: https://doi.org/10.1007/978-3-031-40725-3_12
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