44 citations · 46 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 44 cited
Medoid Silhouette clustering with automatic cluster number selection
Lars Lenssen, Erich Schubert
The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measu…
cs.LG2023★ 1 cited
Sparse Partitioning Around Medoids
Lars Lenssen, Erich Schubert
Partitioning Around Medoids (PAM, k-Medoids) is a popular clustering technique to use with arbitrary distance functions or similarities, where each cluster is represented by its mo…
stat.ML2023★ 1 cited
Data Aggregation for Hierarchical Clustering
Erich Schubert, Andreas Lang
Hierarchical Agglomerative Clustering (HAC) is likely the earliest and most flexible clustering method, because it can be used with many distances, similarities, and various linkag…