R Tutorial 35: K-means, Spectral Clustering, Principal Component Analysis (no package used)


This video is going to talk about how to perform k-means algorithm, spectral clustering, Principal Component Analysis for classification/clustering problem in R without calling any package.These algorithms are all for clustering/classification problem. For k-means, I calculate the distances between each data point to individual centroid. For spectral clustering, I use Gaussian Kernel to create Similarity matrix, and normalized Laplacian to calculate eigenvectors/eigenvalues. For principal component analysis, I also use eigenvector to calculate loadings and data variability explained.

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