قارن الطرق

	التجميع الهرمي الكثافي شبه المُشرف عليه (Semi-supervised HDBSCAN)	تجميع K-means	دي بي إس كان شبه المُشرف عليه
المجال	تعلم الآلة	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning	Machine learning
سنة النشأة	2017–present	1967 (formalized 1982)	2000s
صاحب الطريقة	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors	MacQueen, J. B.; Lloyd, S. P.	Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s)
النوع	Semi-supervised density-based clustering	Partitional clustering	Constrained density-based clustering
المصدر التأسيسي	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press.
الأسماء البديلة	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Constrained DBSCAN, SS-DBSCAN, DBSCAN with must-link/cannot-link constraints, seeded DBSCAN
ذات صلة	6	4	5
الملخص	Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge.	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.	Semi-supervised DBSCAN extends the canonical density-based clustering algorithm (Ester et al., 1996) by incorporating a small set of pairwise or label constraints — must-link pairs that must share a cluster, cannot-link pairs that must be separated, or a handful of known labels — to guide cluster formation while retaining DBSCAN's ability to discover arbitrary-shaped clusters and flag noise points.