Clustering Geo-data Cubes

Tool to perform cluster analysis of multi-dimensional geospatial data, running on local or distributed systems.

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336 commits | Last update: September 23, 2021

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What Clustering Geo-data Cubes can do for you

  • Tool to perform co- and tri-cluster analysis
  • Targets geospatial data sets
  • Includes functionalities to refine the clustering using k-means
  • Multiple implementations to work with local or distributed systems

Clustering Geo-data Cubes (CGC) includes various implementations of co- and tri-clustering algorithms to efficiently carry out cluster analysis on both small and large data sets, using either local or distributed resources. Tutorials illustrate how to use this Python tool with real-world geospatial raster data.

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Tags
  • Big data
  • Machine learning
Programming Language
  • Python
License
  • Apache-2.0
Source code

Contributors

  • Francesco Nattino
    Netherlands eScience Center
  • Ou Ku
    Netherlands eScience Center
  • Meiert Grootes
    Netherlands eScience Center
  • Romulo Gonçalves
    Netherlands eScience Center
  • Emma Izquierdo-Verdiguier
    Institute of Geomatics, BOKU
  • Serkan Girgin
    University of Twente
  • Raul Zurita Milla
    University of Twente
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Contact person
Francesco Nattino
Netherlands eScience Center

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