SPCA 2.0
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)
引用格式
Tarik Benkaci (2024). SPCA 2.0 (https://github.com/TBenkHyd2/PCA), GitHub. 检索来源 .
MATLAB 版本兼容性
平台兼容性
Windows macOS Linux标签
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!无法下载基于 GitHub 默认分支的版本
版本 | 已发布 | 发行说明 | |
---|---|---|---|
2.1 | in SPCA 2.1 Accept Number of variables: 4, 5 and more
|
|
|
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 |
|