SPCA 2.0

版本 2.1 (488.3 KB) 作者: Tarik Benkaci
Principal Component Analysis For Spatial Data (SPCA 2.1) and Clustering of observations by three methods: KNN, K-means, HC.
72.0 次下载
更新时间 2021/3/11

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
创建方式 R2014a
兼容 R2012a 到 R2020b 的版本
平台兼容性
Windows macOS Linux

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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
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

要查看或报告此来自 GitHub 的附加功能中的问题,请访问其 GitHub 仓库
要查看或报告此来自 GitHub 的附加功能中的问题,请访问其 GitHub 仓库