Σύγκριση μεθόδων

	Αυτο-επιβλεπόμενο DBSCAN	Ομαδοποίηση K-means
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2018–2021	1967 (formalized 1982)
Δημιουργός	Ester et al. (DBSCAN base); pipeline pattern established in multiple works c. 2018–2021	MacQueen, J. B.; Lloyd, S. P.
Τύπος	Two-stage pipeline (self-supervised pre-training + density-based clustering)	Partitional clustering
Θεμελιώδης πηγή	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Εναλλακτικές ονομασίες	SSL-DBSCAN, self-supervised density clustering, contrastive DBSCAN, representation-based DBSCAN	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Συναφείς	5	4
Σύνοψη	Self-supervised DBSCAN is a two-stage unsupervised pipeline that first trains a neural encoder on a pretext task — such as contrastive learning or masked reconstruction — to produce compact, semantically meaningful embeddings from unlabeled data, and then applies DBSCAN in the resulting embedding space to discover arbitrarily shaped clusters without requiring any class labels.	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.