مقایسهٔ روشها
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| دیبیاسکن× | ماشین بردار پشتیبان تک کلاسه× | |
|---|---|---|
| حوزه | یادگیری ماشین | یادگیری ماشین |
| خانواده | Machine learning | Machine learning |
| سال پیدایش≠ | 1996 | 1999–2001 |
| پدیدآور≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| نوع≠ | Density-based clustering algorithm | Anomaly / novelty detection (unsupervised) |
| منبع بنیادین≠ | 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 ↗ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ |
| نامهای دیگر≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| مرتبط | 3 | 3 |
| خلاصه≠ | 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. | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. |
| ScholarGateمجموعهداده ↗ |
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