Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms

By Alan P. Reynolds, Graeme Richards, Beatriz de la Iglesia, Victor J. Rayward-Smith. J. Math. Model. Algorithms, 2006

568 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/33afcf296141e43586790158c5f52bbcfb799b81

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. Rob Knight
2. Amanda Birmingham
3. Antonio Gonzalez
4. Yoshiki Vazquez-Baeza
5. Zhenjiang Xu
6. Catherine A. Lozupone
7. Sophie J. Weiss
8. Peter Markewitz

In Information Systems

1. Paul J. McMurdie
2. Susan P. Holmes

In Signal Processing

1. Paul J. McMurdie
2. Rob Knight
3. Susan P. Holmes

Citations in the corpus

• Impact of different time series aggregation methods on optimal energy system design (2017, methodology): “[32] use a greedy optimization algorithm called Partitioning Around Medoids (PAM) [40], the problem can also be stated as a Mixed-Integer Linear Program (MILP) [33, 35], for which good global solving algorithms exist.”
• Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible (2013, methodology): “…Euclidean PoissonDist top - MSD UniFrac - u UniFrac - w ll l l l l l ll l l l l l ll l l l l l ll l l l l l ll l l l l l ll l l l l l l l l l l l l l l l l l l l l l l l l l l 0.6 0.8 [50,51] clustering accuracy (vertical axis) that results following different normalization and distance methods.”
• Normalization and microbial differential abundance strategies depend upon data characteristics (2017, methodology): “methods by the proportion of samples correctly classified into the two clusters by the partitioning around medioids (PAM) algorithm [73, 74].”