SPCA 2.0

Version 2.1 (488 KB) by Tarik Benkaci
Principal Component Analysis For Spatial Data (SPCA 2.1) and Clustering of observations by three methods: KNN, K-means, HC.
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Updated 11 Mar 2021
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SPCA 2.0 calculates PCA using Correlation coefficient of Pearson, in addition there is clustering of observations by three methods: KNN, K-means and Hierarchical Clustering.
The code displays main calculations of PCA : Correlation matrix (using c.pearson) and computes eigenvectors and eigenvalues.
in second part: the package displays Clustering of Observations according three methods: KNN, K-means and Hierarchical clustering (HC)

Cite As

Tarik Benkaci (2024). SPCA 2.0 (https://github.com/TBenkHyd2/PCA), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2014a
Compatible with R2012a to R2020b
Platform Compatibility
Windows macOS Linux

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Versions that use the GitHub default branch cannot be downloaded

Version Published Release Notes
2.1

in SPCA 2.1 Accept Number of variables: 4, 5 and more
Displays Cluster for each Observation
2.0

calculates Principal Component Analysis and clustering (PCA) Observations with 3 methods
1.2

Spatial Principal Component Analysis (SPCA 1.1), in addition there is clustering of observations by three methods: KNN, K-means and Hierarchical Clustering.

1.1.0

The package calculates PCA using Correlation coefficient of Pearson, in addition (SPCA 1.1) there is clustering of observations by three methods: KNN, K-means and Hierarchical Clustering.
1.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.