The field of clustering and geometric computations is witnessing significant developments, driven by the need for efficient and accurate analysis of complex data. A key trend is the focus on improving traditional center-based clustering algorithms, such as K-means, to handle complex datasets. Novel methods are being proposed to optimize these algorithms, leading to improved accuracy and robustness. Additionally, researchers are exploring new approaches to clustering, including those based on nearest neighbors and equilibrium conditions. Geometric computations are also advancing, with efficient algorithms being developed for tasks such as illuminating non-overlapping circular discs and computing directional extremal boundaries. Noteworthy papers include:

• One that proposes a general optimization method for center-based clustering algorithms, resulting in an average accuracy improvement of 33.4% and 64.1%.
• Another that introduces a novel nearest neighbours based clustering algorithm, allowing for fully automatable model selection and producing high-quality clustering solutions.