השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| DBSCAN מוסבר× | Explainable Isolation Forest× | HDBSCAN× | |
|---|---|---|---|
| תחום | למידת מכונה | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning | Machine learning |
| שנת המקור≠ | 1996 (DBSCAN); 2010s (XAI integration) | 2008 / 2017 | 2013 |
| הוגה השיטה≠ | Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP) | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer) | Campello, R. J. G. B.; Moulavi, D.; Sander, J. |
| סוג≠ | Unsupervised clustering with post-hoc interpretability | Anomaly detection with post-hoc explainability | Hierarchical density-based clustering |
| מקור מכונן≠ | 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 Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. AAAI Press. link ↗ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg. DOI ↗ |
| כינויים | XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN* |
| קשורות≠ | 5 | 5 | 3 |
| תקציר≠ | Explainable DBSCAN pairs the DBSCAN density-based clustering algorithm with post-hoc interpretability methods — most commonly SHAP values or local surrogate models — to reveal which input features drive the algorithm's cluster and noise assignments. It enables analysts to understand why specific points were grouped together or flagged as outliers, bridging the gap between powerful density-based partitioning and human-readable explanation. | Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains. | HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions. |
| ScholarGateמערך נתונים ↗ |
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