Sammenlign metoder

	OPTICS	Hierarkisk gruppering
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1999	1963
Ophavsperson	Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J.	Ward, J. H.
Type	Density-based clustering (reachability ordering)	Unsupervised clustering (agglomerative)
Oprindelig kilde	Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2), 49–60.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Aliasser	OPTICS, Ordering Points To Identify the Clustering Structure, density-based clustering with reachability plot, generalized DBSCAN	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Relaterede	3	4
Resumé	OPTICS (Ordering Points To Identify the Clustering Structure) is a density-based clustering algorithm introduced by Ankerst, Breunig, Kriegel, and Sander in 1999. It generalizes DBSCAN by processing points in an ordering that encodes the full density-based cluster structure of a dataset, enabling the detection of clusters of varying densities through a reachability plot rather than requiring a fixed global density threshold.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.