OPTICS algorithm

OPTICS is an algorithm for finding cluster in spatial data. It was presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg SanderMihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander (1999). "OPTICS: Ordering Points To Identify the Clustering Structure". ACM SIGMOD international conference on Management of data. ACMI Press. pp. 49–60. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)CS1 maint: multiple names: authors list (link) . In its basic idea, it is similar to DBSCAN, but addresses one of DBSCAN's major weaknesses: the problem to detect meaningful cluster in data of varying density. In order to do so, the points of the database are (linearily) ordered such that points which are spatially closest become neighbors in the ordering. Additionally, a special distance is stored for each point that represents the density that needs to be accepted for a cluster in order to have both points belong to the same cluster. This is best represented in a dendrogram.