DBSCAN: Difference between revisions
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DBSCAN (Density-based spatial clustering of applications with noise), is a density-based clustering algorithm used for examining spatial data.<ref name="dbscanpy">[https://www.coursera.org/learn/machine-learning-with-python/lecture/B8ctK/dbscan DBSCAN]Coursera</ref> | '''DBSCAN (Density-based spatial clustering of applications with noise)''', is a density-based clustering algorithm used for examining spatial data.<ref name="dbscanpy">[https://www.coursera.org/learn/machine-learning-with-python/lecture/B8ctK/dbscan DBSCAN]Coursera</ref> | ||
== Motivation == | |||
DBSCAN is particularly effective for tasks like class identification on a spatial context. The DBSCAN algorithm can find out any arbitrary shaped cluster without getting effected by noise.<ref name="dbscanpy"/> | |||
== References == | == References == | ||
Latest revision as of 02:50, 1 April 2020
DBSCAN (Density-based spatial clustering of applications with noise), is a density-based clustering algorithm used for examining spatial data.[1]
Motivation
DBSCAN is particularly effective for tasks like class identification on a spatial context. The DBSCAN algorithm can find out any arbitrary shaped cluster without getting effected by noise.[1]