השוואת שיטות

	DBSCAN	HDBSCAN	אשכול K-means
תחום	למידת מכונה	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning
שנת המקור	1996	2013	1967 (formalized 1982)
הוגה השיטה	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	MacQueen, J. B.; Lloyd, S. P.
סוג	Density-based clustering algorithm	Hierarchical density-based clustering	Partitional clustering
מקור מכונן	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.	Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
כינויים	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
קשורות	3	3	4
תקציר	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.	HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.