OPTICS, which stands for Ordering Points To Identify the Clustering Structure, is a density-based clustering algorithm designed to identify clusters in data with varying densities. Developed by the same research group that created DBSCAN, OPTICS addresses the limitations of its predecessor by not requiring a fixed distance parameter, allowing it to adapt to different cluster shapes and sizes. The algorithm generates a reachability distance plot that visually represents the clustering structure, making it easier to analyze complex datasets. This flexibility makes OPTICS particularly useful in fields such as data mining and machine learning, where diverse data distributions are common.