2026/07/01 by Diaa Azzam, Muhammad Azam, Nizar Bouguila
Computer Science · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference
paper · doi:10.1002/ima.70397
openalex publication_date 2026/07/01 · openalex created_date 2026/07/22 · openalex updated_date 2026/07/22
ABSTRACT Clustering is a foundational paradigm in data mining and pattern recognition aimed at grouping data and uncovering meaningful clusters. This data being clustered can be bounded and exhibit non‐Gaussian characteristics in feature spaces, where traditional approaches may contend challenges. Moreover, the presence of irrelevant features can obscure latent structure, undermining both cluster quality and downstream decision‐making. In this work, we address these challenges by proposing a Bayesian Libby‐Novick Beta mixture model (BLNBMM) with integrated feature selection. To enable posterior inference in our proposed hierarchical model, we develop a variational inference (VI) framework that provides uncertainty quantification. Our model flexibly captures relevant features using the Libby‐Novick Beta distribution. Experiments on medical datasets with varying complexity demonstrate that BLNBMM effectively captures complex class distributions in bounded data domains.