A new approach to Density-Based Clustering Models for Open Cluster membership

The purpose of this talk is to take a different approach to density-based machine learning clustering models for selecting members of open clusters. This arises from the need to avoid potential biases caused by multiple densities present in the datasets and the high dependence on the initial selection of hyperparameters. In this seminar, we will evaluate the origins of the problem, a potential solution, and its application to the Coma Berenices and Hyades clusters.