Partitioning Around Medoids (Program PAM)

By L. Kaufman, Peter J. Rousseeuw. 2008

712 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Management Science and Operations Research, Organizational Behavior and Human Resource Management, Statistics and Probability, Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Erich Schubert
2. Peter J. Rousseeuw

In Management Science and Operations Research

1. Erich Schubert
2. Peter J. Rousseeuw

In Organizational Behavior and Human Resource Management

1. Erich Schubert
2. Peter J. Rousseeuw

In Statistics and Probability

1. Erich Schubert
2. Peter J. Rousseeuw
3. Mia Hubert
4. Charu C. Aggarwal
5. S. Van Aelst
6. Peter Filzmoser
7. Christophe Croux
8. Ö. G. Alma

In Algorithms & Complexity

1. Erich Schubert
2. Peter J. Rousseeuw

Citations in the corpus

• Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms (2018, methodology): “Nevertheless, a few seminal methods such as hierarchical clustering, k-means, PAM [9, 11], and DBSCAN [5] have received repeated and widespread use.”