OPTICS algorithm

Part of a series on
Machine learning
and data mining
Paradigms
Problems
Clustering
Dimensionality reduction
Structured prediction
Anomaly detection
Artificial neural network
Reinforcement learning
Learning with humans
Model diagnostics
Mathematical foundations
Journals and conferences
Related articles

Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based[1] clusters in spatial data. It was presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander.[2] Its basic idea is similar to DBSCAN,[3] but it addresses one of DBSCAN's major weaknesses: the problem of detecting meaningful clusters in data of varying density. To do so, the points of the database are (linearly) ordered such that spatially closest points become neighbors in the ordering. Additionally, a special distance is stored for each point that represents the density that must be accepted for a cluster so that both points belong to the same cluster. This is represented as a dendrogram.

  1. ^ Kriegel, Hans-Peter; Kröger, Peer; Sander, Jörg; Zimek, Arthur (May 2011). "Density-based clustering". Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 1 (3): 231–240. doi:10.1002/widm.30. S2CID 36920706.
  2. ^ Mihael 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. ACM Press. pp. 49–60. CiteSeerX 10.1.1.129.6542.
  3. ^ Martin Ester; Hans-Peter Kriegel; Jörg Sander; Xiaowei Xu (1996). Evangelos Simoudis; Jiawei Han; Usama M. Fayyad (eds.). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press. pp. 226–231. CiteSeerX 10.1.1.71.1980. ISBN 1-57735-004-9.