Hierarchical Density-Based Spatial Clustering of Applications with Noise
URI: https://gptkb.org/entity/Hierarchical_Density-Based_Spatial_Clustering_of_Applications_with_Noise
GPTKB entity
Statements (26)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
|
| gptkbp:abbreviation |
gptkb:HDBSCAN
|
| gptkbp:application |
data analysis
pattern recognition anomaly detection |
| gptkbp:basedOn |
gptkb:DBSCAN
|
| gptkbp:citation |
Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In Advances in Knowledge Discovery and Data Mining (pp. 160-172).
|
| gptkbp:feature |
extracts flat clustering from hierarchy
finds clusters of varying densities identifies noise points produces a hierarchy of clusters |
| gptkbp:field |
gptkb:machine_learning
data mining |
| https://www.w3.org/2000/01/rdf-schema#label |
Hierarchical Density-Based Spatial Clustering of Applications with Noise
|
| gptkbp:input |
gptkb:Metric
minimum cluster size minimum samples |
| gptkbp:introduced |
Campello, Moulavi, Sander
|
| gptkbp:introducedIn |
2013
|
| gptkbp:openSource |
Python hdbscan library
|
| gptkbp:output |
cluster labels
noise labels |
| gptkbp:relatedTo |
gptkb:OPTICS
gptkb:DBSCAN |
| gptkbp:bfsParent |
gptkb:HDBSCAN
|
| gptkbp:bfsLayer |
7
|