This article talks about Ordering Points to Identify the Clustering Structure (OPTICS), which is not a clustering technique per se as it does not output clusters. Instead, it presents intrinsic clustering structure that could otherwise be identified only in a process of repeated clustering with different parameter settings. OPTICS successfully differentiates significant objects from noise, identifying all possible levels of clusters within a data set as explained in this section. The basic approach of OPTICS is similar to DBSCAN, but instead of maintaining a set of known, but so far unprocessed cluster members, a priority queue is used.
OPTICS Algorithm
