OPTICS, which stands for Ordering Points To Identify the Clustering Structure, is a density-based unsupervised learning algorithm designed for identifying clusters in data. Developed by the same research group that created DBSCAN, OPTICS addresses the limitations of DBSCAN, particularly its struggle with varying density clusters. Unlike DBSCAN, which relies on a fixed distance parameter, OPTICS evaluates the local density of points, allowing it to adaptively identify clusters of different shapes and sizes. This flexibility makes OPTICS particularly useful in complex datasets where traditional clustering methods may fail to accurately capture the underlying structure.