Research on Clustering Algorithms focuses on developing advanced techniques to group similar data points and uncover meaningful patterns in diverse datasets. Recent advancements include elastic net clustering with dynamic parameters to improve solution quality and computational efficiency, grid-based clustering algorithms like GCAE for handling clusters of varying densities, and torque clustering methods that support autonomous AI and unsupervised learning. High-degree graph-based clustering (HDCluster) enhances performance on graph-structured data, while parallel K-means approaches with dimensionality reduction optimize clustering of high-dimensional text data. Collectively, these innovations improve the accuracy, scalability, and applicability of clustering methods across complex and large-scale datasets.