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| DBSCAN× | K-Means-Clustering× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1996 | 1967 |
| Urheber≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | MacQueen, J. |
| Typ≠ | Density-based clustering algorithm | Partitional clustering (centroid-based) |
| 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 ↗ | MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. link ↗ |
| Aliasnamen≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| Verwandt | 3 | 3 |
| 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 Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis. |
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