OPTICS, which stands for Ordering Points To Identify the Clustering Structure, is a density-based unsupervised learning algorithm used for clustering data points. 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 it to adaptively determine cluster structures. This flexibility makes OPTICS particularly useful in complex datasets where clusters may not be uniformly distributed, enhancing its applicability in various fields such as data analysis and machine learning.