방법 비교

	자기 지도 학습 기반 DBSCAN	DBSCAN	K-means 군집화
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	2018–2021	1996	1967 (formalized 1982)
창시자	Ester et al. (DBSCAN base); pipeline pattern established in multiple works c. 2018–2021	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J. B.; Lloyd, S. P.
유형	Two-stage pipeline (self-supervised pre-training + density-based clustering)	Density-based clustering algorithm	Partitional clustering
원전	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.	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
별칭	SSL-DBSCAN, self-supervised density clustering, contrastive DBSCAN, representation-based DBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
관련	5	3	4
요약	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.	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.	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.