Vertaile menetelmiä

	Itseohjautuva DBSCAN	K-means-klusterointi	Itseohjautuva oppiminen
Tieteenala	Koneoppiminen	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning
Syntyvuosi	2018–2021	1967 (formalized 1982)	2018–2020
Kehittäjä	Ester et al. (DBSCAN base); pipeline pattern established in multiple works c. 2018–2021	MacQueen, J. B.; Lloyd, S. P.	LeCun, Y. and community (formalized ~2018–2020)
Tyyppi	Two-stage pipeline (self-supervised pre-training + density-based clustering)	Partitional clustering	Representation learning paradigm
Alkuperäislähde	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.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Rinnakkaisnimet	SSL-DBSCAN, self-supervised density clustering, contrastive DBSCAN, representation-based DBSCAN	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Liittyvät	5	4	3
Tiivistelmä	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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.