השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| Local Outlier Factor (LOF)× | DBSCAN× | מכונת וקטורים תומכים חד-מחלקתית (One-Class SVM)× | |
|---|---|---|---|
| תחום | למידת מכונה | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning | Machine learning |
| שנת המקור≠ | 2000 | 1996 | 1999–2001 |
| הוגה השיטה≠ | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| סוג≠ | Density-based anomaly detection (unsupervised) | Density-based clustering algorithm | Anomaly / novelty detection (unsupervised) |
| מקור מכונן≠ | Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104. DOI ↗ | 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 ↗ |
| כינויים≠ | LOF, local outlier factor, density-based outlier detection, local density deviation | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| קשורות≠ | 4 | 3 | 3 |
| תקציר≠ | Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space. | 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. |
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