2025/11/27 by Guerard, Guillaume, Djebali, Sonia
Computer Science · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Benchmarking #Big data #CURE data clustering algorithm #Categorical variable #Cluster analysis #Clustering high-dimensional data #Consensus clustering #Exploit #FOS: Computer and information sciences #Hierarchical clustering #Machine Learning (cs.LG) #Stochastic Gradient Optimization Techniques
paper · open access · doi:10.48550/arxiv.2512.03070
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/11/27 · openalex created_date 2025/12/05 · openalex updated_date 2026/07/28
The advent of the big data paradigm has transformed how industries manage and analyze information, ushering in an era of unprecedented data volume, velocity, and variety. Within this landscape, mixed-data clustering has become a critical challenge, requiring innovative methods that can effectively exploit heterogeneous data types, including numerical and categorical variables. Traditional clustering techniques, typically designed for homogeneous datasets, often struggle to capture the additional complexity introduced by mixed data, underscoring the need for approaches specifically tailored to this setting. Hierarchical and explainable algorithms are particularly valuable in this context, as they provide structured, interpretable clustering results that support informed decision-making. This paper introduces a clustering method grounded in pretopological spaces. In addition, benchmarking against classical numerical clustering algorithms and existing pretopological approaches yields insights into the performance and effectiveness of the proposed method within the big data paradigm.