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| DBSCAN× | K-means Clustering× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1996 | 1967 (formalized 1982) |
| Urheber≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | MacQueen, J. B.; Lloyd, S. P. |
| Typ≠ | Density-based clustering algorithm | Partitional clustering |
| Wegweisende Quelle≠ | 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. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Aliasnamen≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Verwandt≠ | 3 | 4 |
| Zusammenfassung≠ | 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. |
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