Work

CGC: a Scalable Python Package for Co- and Tri-Clustering of Geodata Cubes

Year: 2022

Type: software-paper

Abstract: Multidimensional data cubes are increasingly ubiquitous, in particular in the geosciences.Clustering techniques encompassing their full dimensionality are necessary to identify patterns "hidden" within these cubes.Clustering Geodata Cubes (CGC) is a Python package designed for partitional clustering, which identifies groups of similar data across two (e.g., spatial and temporal) or three (e.g., spatial, temporal, and thematic) dimensions.CGC provides efficient and scalable co-and tri-clustering functionality appropriate to analyze both small and large datasets as well as a cluster refinement functionality that supports users in their quest to make sense of complex datasets.

Source: The Journal of Open Source Software

DOI: 10.21105/joss.04032

Authors: Francesco Nattino, Ou Ku, Meiert W. Grootes, Emma Izquierdo-Verdiguier, Serkan Girgin, Raul Zurita-Milla

Institutions: Netherlands eScience Center, BOKU University, International Institute for Geo-Information Science and Earth Observation, University of Twente

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4223636637

Cites: 25

Cited by: 2

Related to: 10