The field of clustering and deep learning is witnessing significant developments, with a focus on improving the performance and efficiency of clustering algorithms on high-dimensional data. Researchers are exploring new evaluation frameworks and dimensionality reduction techniques to enhance clustering quality. The use of autoencoders is becoming increasingly popular as a sandbox for developing novel deep clustering algorithms, allowing for the learning of low-dimensional, non-linear representations from data without labels. This has led to the creation of more efficient and effective clustering methods, such as tree-guided convex clustering, which can handle large-scale datasets and construct complete clusterpaths quickly. Noteworthy papers include:

• Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data, which introduces a novel evaluation framework for assessing clustering performance across multiple dimensionality reduction techniques.
• Tree-Guided $L_1$-Convex Clustering, which develops a novel convex clustering algorithm that achieves superior computational efficiency without sacrificing clustering performance.