The Average Silhouette Width (ASW) is a popular cluster validation index to estimate the number of clusters. The question whether it also is suitable as a general objective function to be optimized for finding a clustering is addressed. Two algorithms (the standard version OSil and a fast version FOSil) are proposed, and they are compared with existing clustering methods in an extensive simulation study covering known and unknown numbers of clusters. Real data sets are analysed, partly exploring the use of the new methods with non-Euclidean distances. The ASW is shown to satisfy some axioms that have been proposed for cluster quality functions. The new methods prove useful and sensible in many cases, but some weaknesses are also highlighted. These also concern the use of the ASW for estimating the number of clusters together with other methods, which is of general interest due to the popularity of the ASW for this task.

平均轮廓宽度（ASW）是一种流行的聚类验证指标，用于估计聚类数量。解决了它是否还适合作为优化的通用目标函数以找到聚类的问题。提出了两种算法（标准版本OSil和快速版本FOSil），并将它们与现有的聚类方法进行了比较，并进行了广泛的模拟研究，涵盖了已知数量和未知数量的群集。分析了实际数据集，部分探索了非欧氏距离下新方法的使用。所示的ASW满足为集群质量函数提出的一些公理。在许多情况下，新方法被证明是有用且明智的，但同时也突出了一些缺点。这些还涉及使用ASW估算簇数以及其他方法，