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Clustering PCA-HCPC on a quantitative dataset

Perez Joan; Fusco Giovanni

This release allows to perform a combined Principal Component Analysis followed by a Hierarchical Clustering on Principle Components (HCPC) on a quantitative data set.
The test data is Data_India.txt, 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 and at the district level. Data refer to 2011 or to 2001-2011 evolutions.

v.1.1 contains
- R Script (PCA HCPC.R)
- Data set (Data_India.txt)
- Archive of shapefiles for the Indian districts of 2011 (District_2011.zip)
- Graphical outputs for PCA and HCPC
- README.txt
 
References:
[1] JOSSE, Julie; HUSSON, François. missMDA: A Package for Handling Missing Values in Multivariate Data Analysis. Journal of Statistical Software, [S.l.], v. 70, Issue 1, p. 1 - 31,
apr. 2016. ISSN 1548-7660. Available at: <https://www.jstatsoft.org/v070/i01>
[2] Christian Hennig. Flexible Procedures for Clustering, fpc package v2.1-11.1, 2018. https://www.rdocumentation.org/packages/fpc
[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
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