Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
University of California, Los Angeles · Rosetta Stone (United States)
Abstract
SUMMARY: Hierarchical clustering is a widely used method for detecting clusters in genomic data. Clusters are defined by cutting branches off the dendrogram. A common but inflexible method uses a constant height cutoff value; this method exhibits suboptimal performance on complicated dendrograms. We present the Dynamic Tree Cut R package that implements novel dynamic branch cutting methods for detecting clusters in a dendrogram depending on their shape. Compared to the constant height cutoff method, our techniques offer the following advantages: (1) they are capable of identifying nested clusters; (2) they are flexible-cluster shape parameters can be tuned to suit the application at hand; (3) they are suitable…
Citation impact
- FWCI
- 7.94
- Percentile
- 100%
- References
- 15
Authors
3Topics & keywords
- Dendrogram
- Hierarchical clustering
- Tree (set theory)
- Computer science
- Outlier
- Cluster analysis
- Medoid
- Cluster (spacecraft)