Software Open Access

Robust-SOM-Clustering

Perez Joan; Fusco Giovanni

This release allows performing a combined SOM/SuperSOM clustering of the 640 administrative districts of India.
Fowlkes-Mallows Similarity Index is used to identify robust initializations of the clustering.
Data_India.txt is a specially conceived geographic database of 55 indicators, covering issues of economic activity,
urban structure, socio-demographic development, consumption levels, infrastructure endowment and 
basic geographical positioning within the Indian space. Data refer to 2011 or to 2001-2011 evolutions.
A shapefile of the Indian district is attached allowing to map the results.

v.1.2 contains
- R Script (Robust SOM Clustering v1.2)
- Data set (Data_India.txt)
- Archive of shapefiles for the Indian districts of 2011 (District_2011.zip)
- Fusco G., Perez J., 2015, Spatial Analysis of the Indian Subcontinent: the Complexity Investigated through Neural Networks
- README.txt

# References:
[1] Wehrens R., Buydens L., 2007, Self- and Super-organizing Maps in R: The ko-honen Package,
    Journal of Statistical Software, Vol. 21, Issue 5.
[2] Fusco G., Perez J., 2015, Spatial Analysis of the Indian Subcontinent: the Complexity Investigated 
    through Neural Networks, CUPUM 2015 - 14th International Conference on Computers in Urban Planning 
    and Urban Management, MIT, Cambridge (Ma.), July 5th-7th 2015, Proceedings, 287, 1-20,
    http://web.mit.edu/cron/project/CUPUM2015/proceedings/Content/analytics/287_fusco_h.pdf 
[3] Perez J., 2015, Spatial Structures in India in the Age of Globalisation. A Data-Driven Approach, Phd in geography,
     University of Avignon (France)

This release is part of the Geo-Soft Models project https://zenodo.org/communities/geo-soft-models
Files (5.8 MB)
Name Size
perezjoan/Robust-SOM-SuperSOM-Clustering-v1.2.zip
md5:5a5532daf80eda8a010894414b4c40bf
5.8 MB Download
130
21
views
downloads
All versions This version
Views 130130
Downloads 2121
Data volume 121.7 MB121.7 MB
Unique views 9595
Unique downloads 2020

Share

Cite as