The field of data modeling and clustering is witnessing significant advancements with the integration of innovative mathematical and computational techniques. Recent developments indicate a shift towards the use of pretopology and hypernetwork theory to address complex data challenges. Pretopology-based methods are being explored for clustering mixed data, allowing for the creation of hierarchical and interpretable clusters without the need for dimensionality reduction. Hypernetwork theory, on the other hand, is providing a rigorous foundation for mechanisable multilevel modeling, enabling the representation of hierarchical and heterarchical systems. Additionally, there is a growing interest in applying these techniques to real-world problems, such as energy consumption profiling and customer identification. Noteworthy papers include PretopoMD, which presents a novel pretopology-based algorithm for clustering mixed data, and Hypernetwork Theory: The Structural Kernel, which develops a complete algebra of structural composition for hypernetworks.