Linganisha mbinu

	Uainishaji wa K-means	HDBSCAN yenye usimamizi nusu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1967 (formalized 1982)	2017–present
Mwanzilishi	MacQueen, J. B.; Lloyd, S. P.	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors
Aina	Partitional clustering	Semi-supervised density-based clustering
Chanzo asilia	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.
Majina mbadala	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN
Zinazohusiana	4	6
Muhtasari	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 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.