OPTICS Clustering Algorithm

An OPTICS Clustering Algorithm is a density-based clustering algorithm that linearly orders points, such that points which are spatially closest become neighbors in the ordering.

• AKA: Ordering Points to Identify the Clustering Structure.
  • …
• Counter-Example(s):
  • DBSCAN Algorithm.
  • Mean-Shift Clustering Algorithm.
• See: Centroid-based Clustering, Dendrogram.

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References

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/OPTICS_algorithm Retrieved:2015-1-16.
  • Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based clusters in spatial data. It was presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander. Its basic idea is similar to DBSCAN, but it addresses one of DBSCAN's major weaknesses: the problem of detecting meaningful clusters in data of varying density. In order to do so, the points of the database are (linearly) 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 represented as a dendrogram.

1999

• (Ankerst et al., 1999) ⇒ Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, and Jörg Sander. (1999). “OPTICS: Ordering points to identify the clustering structure.” In: ACM Sigmod Record, 28(2). doi:10.1145/304181.304187

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