CRAD：使用强大的自动切割和深度进行聚类
CRAD: Clustering with Robust Autocuts and Depth
摘要：
我们开发了一种新的基于密度的聚类算法CRAD，它是基于一个新的邻居搜索功能与强大的数据深度作为相异性度量。实验结果表明，新的CRAD算法在检测不同密度的簇时，与现有的算法如DBSCAN、OPTICS和DBCA相比，具有很强的竞争力。此外，一个新的有效的参数选择过程中，选择最佳的底层参数在现实世界的聚类，当地面真理是未知的。最后，我们提出了一个新的聚类框架，扩展CRAD从空间数据聚类到时间序列聚类，而无需先验知识的真正数量的集群。通过大量的实验研究，CRAD的性能进行了评估。
英文摘要：
We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure. Our experiments prove that the new CRAD is highly competitive at detecting clusters with varying densities, compared with the existing algorithms such as DBSCAN, OPTICS and DBCA. Furthermore, a new effective parameter selection procedure is developed to select the optimal underlying parameter in the real-world clustering, when the ground truth is unknown. Lastly, we suggest a new clustering framework that extends CRAD from spatial data clustering to time series clustering without a-priori knowledge of the true number of clusters. The performance of CRAD is evaluated through extensive experimental studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
