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.
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- 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.
- Hypernym
- Algorithm
- Is primary topic of
- OPTICS algorithm
- Label
- enOPTICS algorithm
- Link from a Wikipage to an external page
- hdbscan.readthedocs.io/
- pyclustering.github.io/
- Link from a Wikipage to another Wikipage
- 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 algorithm?oldid=1116998648&ns=0
- WikiPageLength
- 15764
- Wikipage page ID
- 22509799
- Wikipage revision ID
- 1116998648
- WikiPageUsesTemplate
- Template:Machine learning
- Template:Mvar
- Template:Not a typo
- Template:Reflist
- Template:Short description