Machine learning
OPTICS
OPTICS（Ordering Points To Identify the Clustering Structure，排序点以识别聚类结构）是由 Ankerst、Breunig、Kriegel 和 Sander 于 1999 年提出的一种基于密度的聚类算法。它通过处理编码数据集完整基于密度聚类结构的点序列来泛化 DBSCAN，从而能够通过可达性图检测不同密度的聚类，而无需固定的全局密度阈值。
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来源
- Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2), 49–60. DOI: 10.1145/304181.304187 ↗
- 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 International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. link ↗
- Aggarwal, C. C., & Reddy, C. K. (Eds.) (2013). Data Clustering: Algorithms and Applications (Ch. 4). CRC Press. ISBN: 978-1-4665-5821-2
如何引用本页
ScholarGate. (2026, June 3). OPTICS: Ordering Points To Identify the Clustering Structure. ScholarGate. https://scholargate.app/zh/machine-learning/optics
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