LVCluster: Bounded Clustering using Laguerre Voronoi Diagram
Abu Quwsar Ohi, Marina L. Gavrilova · Computer Science Research Notes · 2024
Clustering, a fundamental technique in unsupervised learning, identifies similar groups within a dataset. However, clustering algorithms encounter limitations when requiring a predetermined number of clusters/centroids/labels. This paper proposes a novel approach of clustering by integrating concepts from Voronoi diagrams in Laguerre geometry, namely, Laguerre Voronoi Clustering (LVCluster). Laguerre geometry introduces circles by adding radius weight metric to centroids, enabling dynamic exclusion from clustering criteria. Consequently, this approach offers flexibility by necessitating only one hyperparameter, an upper-bound value for the number of circles. LVCluster can be optimized using gradient descent and can be jointly optimized with deep neural network architectures. The experimental results indicated that LVCluster outperforms clustering algorithms when trained individually and jointly with deep neural networks on increased cluster centroids.