Cluster analysis algorithms

OPTICS algorithm

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. 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. (Wikipedia).

OPTICS algorithm
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Related pages

Spanning tree | Priority queue | Correlation clustering | Fixed-radius near neighbors | Dendrogram | K-d tree | Scikit-learn | Hierarchical clustering | Single-linkage clustering | Anomaly detection | DBSCAN | Cluster analysis | Weka (machine learning) | ELKI | Heap (data structure) | Local outlier factor