4 papers
CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering
Arghya Pratihar, Shinjon Chakraborty, Swagatam Das
We propose \textbf{CaSPECT}, a causal spectral clustering framework for discovering causally homogeneous subgroups from observational data. Rather than clustering in covariate spac…
Hyperbolic Gaussian Blurring Mean Shift: A Statistical Mode-Seeking Framework for Clustering in Curved Spaces
Arghya Pratihar, Arnab Seal, Swagatam Das +1
Clustering is a fundamental unsupervised learning task for uncovering patterns in data. While Gaussian Blurring Mean Shift (GBMS) has proven effective for identifying arbitrarily s…
Hyperbolic Fuzzy C-Means with Adaptive Weight-based Filtering for Efficient Clustering
Swagato Das, Arghya Pratihar, Swagatam Das
Clustering algorithms play a pivotal role in unsupervised learning by identifying and grouping similar objects based on shared characteristics. Although traditional clustering tech…
Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration
Arghya Pratihar, Kushal Bose, Swagatam Das
Clustering aims at partitioning data points into groups of similar objects without knowing about the class labels. However, clustering datasets with complex geometric structures, s…