OPTICS algorithm

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.

Comment
enOrdering 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.
Depiction
OPTICS.svg
Has abstract
enOrdering 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.
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Algorithm
Is primary topic of
OPTICS algorithm
Label
enOPTICS algorithm
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hdbscan.readthedocs.io/
pyclustering.github.io/
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Anomaly detection
Category:Cluster analysis algorithms
Cluster analysis
Correlation clustering
DBSCAN
Dendrogram
ELKI
File:OPTICS.svg
Fixed-radius near neighbors
GNU R
Hans-Peter Kriegel
Heap (data structure)
Hierarchical clustering
K-d tree
Local outlier factor
Priority queue
Scikit-learn
Single-linkage clustering
Spanning tree
Spatial index
Subspace clustering
Weka (machine learning)
SameAs
m.05zxvdx
OPTICS
OPTICS
OPTICS
OPTICS
OPTICS algorithm
Q2007847
utEX
Алгоритм кластеризации OPTICS
Subject
Category:Cluster analysis algorithms
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OPTICS.svg?width=300
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OPTICS algorithm?oldid=1116998648&ns=0
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Wikipage page ID
22509799
Wikipage revision ID
1116998648
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