5 citations · 5 across the 3 of their papers we have counts for
5 papers
UniFair: A unified fair clustering approach based on separation and compactness
Antonia Karra, Vasiliki Papanikou, Georgios Vardakas +2
Clustering is increasingly used to support high-impact decisions, yet standard objectives such as k-means can produce clusterings that treat demographic groups unequally. Existing…
Universal description of the Neutron Star's surface and its key global properties: A Machine Learning Approach for nonrotating and rapidly rotating stellar models
Grigorios Papigkiotis, Georgios Vardakas, Aristidis Likas +1
Neutron stars provide an ideal theoretical framework for exploring fundamental physics when nuclear matter surpasses densities encountered within atomic nuclei. Despite their param…
Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters
Georgios Vardakas, Ioannis Papakostas, Aristidis Likas
Unsupervised learning has gained prominence in the big data era, offering a means to extract valuable insights from unlabeled datasets. Deep clustering has emerged as an important…
Revisiting Silhouette Aggregation
John Pavlopoulos, Georgios Vardakas, Aristidis Likas
Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment. To assess…
UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters
Georgios Vardakas, Argyris Kalogeratos, Aristidis Likas
Estimating the number of clusters k while clustering the data is a challenging task. An incorrect cluster assumption indicates that the number of clusters k gets wrongly estimated.…