Порівняння методів

	ОПТИКА	DBSCAN	Ієрархічна кластеризація
Галузь	Машинне навчання	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning	Machine learning
Рік появи	1999	1996	1963
Автор методу	Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ward, J. H.
Тип	Density-based clustering (reachability ordering)	Density-based clustering algorithm	Unsupervised clustering (agglomerative)
Основоположне джерело	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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Інші назви	OPTICS, Ordering Points To Identify the Clustering Structure, density-based clustering with reachability plot, generalized DBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Пов'язані	3	3	4
Підсумок	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.