5 citations · 5 across the 11 of their papers we have counts for
8 papers · 1 filter
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…
Composite Silhouette: A Subsampling-based Aggregation Strategy
Aggelos Semoglou, Aristidis Likas, John Pavlopoulos
Determining the number of clusters is a central challenge in unsupervised learning, where ground-truth labels are unavailable. The Silhouette coefficient is a widely used internal…
Silhouette-Driven Instance-Weighted -means
Aggelos Semoglou, Aristidis Likas, John Pavlopoulos
Clustering is a fundamental unsupervised learning task with applications across a wide range of domains. Popular algorithms such as -means are efficient and widely used, but can…
Counterfactual Explanations for k-means and Gaussian Clustering
Georgios Vardakas, Antonia Karra, Evaggelia Pitoura +1
Counterfactuals have been recognized as an effective approach to explain classifier decisions. Nevertheless, they have not yet been considered in the context of clustering. In this…
Statistical Modeling of Univariate Multimodal Data
Paraskevi Chasani, Aristidis Likas
Unimodality constitutes a key property indicating grouping behavior of the data around a single mode of its density. We propose a method that partitions univariate data into unimod…
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.…