Multivariate clustering analysis

Knowledge graph centered on Multivariate clustering analysis with 29 nodes and 120 connections. Top connected: Gothic, Beetles, Crested Butte, Populus tremuloides, East River.

Method synopsis

Applies multiple clustering algorithms (K-means, hierarchical clustering, Gaussian mixture models) to hillslope feature vectors to identify watershed zones with similar characteristics. Uses model selection criteria to determine optimal number of clusters.

Synthesized from method descriptions across 2 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions (2024), Frontiers in Water. Implementations in other papers (listed below) may differ.

1. Gaussian Mixture Model clustering
   Extended approach using Gaussian Mixture Model to define zones in multivariable space that capture co-variability among hillslope features. Used Mclust package in R with expectation maximization algorithm. Applied spherical covariance function to update model weights, covariance, and means with each iteration.
   Quantities: 5 optimal clusters identified, processing 125 hillslopes with 6 primary variablesDuration: Iterative process until convergenceConditions: Applied to standardized hillslope feature data
   Equipment: R software, Mclust package

Typical Equipment

• R software with kmeans function
• hclust function
• mclust function

Output Measurements

• zone assignments
• cluster centers
• silhouette scores
• BIC values

Papers Using This Protocol (2)

Species Studied With This Protocol

Places Where Used