Clustering Algorithm

Clustering algorithms aim to group similar data points together, revealing underlying structure and patterns within datasets. Current research emphasizes improving scalability and efficiency for large datasets, exploring novel approaches like those based on graph neural networks, cellular automata, and dimensionality reduction techniques such as UMAP, often combined with established methods like k-means and DBSCAN. These advancements are crucial for diverse applications, ranging from image analysis and text processing to high-energy physics and urban planning, enabling more effective data exploration and knowledge discovery.