Mixed Data Clustering Survey and Challenges

Abstract

The advent of the big data paradigm has revolutionized the way industries handle and analyze information, ushering in an era characterized by unprecedented volumes, velocities, and varieties of data. In this context, mixed data clustering emerges as a critical challenge, necessitating innovative approaches to effectively harness the wealth of heterogeneous data types, including numerical and categorical variables. Traditional methods, designed for homogeneous datasets, often fall short in accommodating the complexities introduced by mixed data, highlighting the need for novel clustering techniques tailored to this context. Hierarchical and explainable algorithms play a pivotal role in addressing these challenges, offering structured frameworks that enable interpretable clustering results, which are essential for informed decision-making. This paper presents a method based on pretopological spaces. Moreover, benchmarking against traditional numerical clustering methods and pretopological approaches provides valuable insights into the performance and efficacy of our novel clustering algorithm within the big data paradigm.