一种数据驱动的层次聚类中聚类数估计方法
A Data-Driven Approach to Estimating the Number of Clusters in Hierarchical Clustering
摘要：
我们提出了两种新的方法来估计聚类的数量在一个层次聚类框架中，希望创建一个完全自动化的过程，没有人为干预。这些方法完全是数据驱动的，不需要研究人员的输入，因此是完全自动化的。它们很容易实现，而且一点也不需要大量的计算。我们分析了几个模拟数据集和生物基基因表达集的性能，比较我们的方法建立差距统计和肘的方法，并在多集群的情况下优于两者。
英文摘要：
We propose two new methods for estimating the number of clusters in a hierarchical clustering framework in the hopes of creating a fully automated process with no human intervention. The methods are completely data-driven and require no input from the researcher, and as such are fully automated. They are quite easy to implement and not computationally intensive in the least. We analyze performance on several simulated data sets and the Biobase Gene Expression Set, comparing our methods to the established Gap statistic and Elbow methods and outperforming both in multi-cluster scenarios.
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。
