A new partitioning around medoids algorithm

By M. J. van der Laan, Katherine S. Pollard, J. Bryan. 2003

311 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4579961fb9d5f77133b5a8a91b0e058d4b5023be

How researchers see this paper

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

In Artificial Intelligence

1. Roberto Romero
2. Xue-wen Chen
3. Sorin Draghici
4. Chi-Hyuck Jun
5. Adi L. Tarca
6. Vincent J. Carey
7. Iqbal H. Sarker
8. Christoph H. Lampert

In Molecular Biology

1. Adi L. Tarca
2. Roberto Romero
3. Sorin Draghici
4. Vincent J. Carey
5. Purvesh Khatri
6. Jianguo Xia
7. Alberto Domingo Pascual-Montano
8. David S. Wishart

Citations in the corpus

• A simple and fast algorithm for K-medoids clustering (2009, background): “Ng and Han (1994) proposed an efficient PAM-based algorithm, which updates new medoids from some neighboring objects. van der Laan, Pollard, and Bryan (2003) tried to maximize the silhouette proposed by Rousseeuw (1987) instead of minimizing the sum of distances to the closest medoid in PAM.”
• Machine Learning and Its Applications to Biology (2007, background): “The robustness is particularly important in the common situation in which many elements do not have a clearcut membership to any specific cluster [31].”