Clustering

Coarse-Grain Cluster Analysis of Tensors With Application to Climate Biome Identification

A tensor provides a concise way to codify the interdependence of complex data. Treating a tensor as a d-way array, each entry records the interaction between the different indices. Clustering provides a way to parse the complexity of the data into …

Machine Learning Safety with Applications to the Climate Sciences

Rapid progress in machine learning (ML) has engendered numerous applications across the sciences. Deployment of modern ML systems have increased our ability to validate and automate the scientific process, broadening the space for discovery. However, …

Coarse-Grain Cluster Analysis of Tensors With Application to Climate Biome Identification

A tensor provides a concise way to codify the interdependence of complex data. Treating a tensor as a d-way array, each entry records the interaction between the different indices. Clustering provides a way to parse the complexity of the data into …

Multiresolution Cluster Analysis—Addressing Trust in Climate Classification

The Earth's climate is a complex system of micro and macroscopic interdependencies. Classifying the surface of The Earth into climate biomes is a way to parse this information down to provide meaningful diagnostics to relate the physical and …

Climate Biome Clustering

This work aims to determine climate biomes directly from data, determine where they are changing, and assign a value of trust to our classification.

Tensor Factorizations of Southern Ocean

The goal of this work is to discover and analyze climatic signatures within E3SM MPAS-O data of the Southern Ocean.