OPTICS, which stands for Ordering Points To Identify the Clustering Structure, is a density-based unsupervised learning algorithm used for clustering data. Developed by the same research group that created DBSCAN, OPTICS addresses the limitations of DBSCAN by effectively identifying clusters in datasets with varying densities. Unlike traditional clustering methods that rely on a fixed distance parameter, OPTICS evaluates the local density of data points, allowing for a more flexible and accurate representation of the underlying structure. This makes it particularly useful in scenarios where clusters are not uniformly distributed, enhancing the analysis of complex datasets.