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Clustering is descriptive: a central point in each cluster serves as a surrogate, or approximate descriptor of, the points in the cluster. Use the coordinates of these central points for labels. As an idea for consideration--certainly not as the only or even the best approach--you could assess how far each central coordinate is from a center of all the ...


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In a word: No. You'll need to go through the cluster by hand and try to spot patterns.


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Scaling is done on the column. It subtracts the mean and divides by the standard deviation , so you should get colMeans(golub) to be around zero. However you don't need to scale, if you check the vignette: library(multtest) ?golub Gene expression data (3051 genes and 38 tumor mRNA samples) from the leukemia microarray study of Golub et al. (1999). Pre-...


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It just means taking the mean of the data. You can do this by finding the mean of each marginal distribution and putting the marginal means into a vector that is the multivariate mean. Example: Let $X=(X_1, X_2, X_3)$ have $\bar{X}_1=3$, $\bar{X}_2=-13$, and $\bar{X}_3=-5$. The the multivariate mean is $\bar{X} = (3, -13, -5)$. (I think this assumes using $...


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I would rather go for Gaussian Mixtures Models, you can think of it like multiple Gaussian distribution based on probabilistic approach, you still need to define the K parameter though, the GMMS handle non-spherical shaped data as well as other forms, here is an example using scikit: https://jakevdp.github.io/PythonDataScienceHandbook/05.12-gaussian-mixtures....


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Elbow method is a heuristic. There's no "mathematical" definition and you cannot create algorithm for it, because the point of the method is about visually finding the "breaking point" on the plot. This is subjective criteria and it often happens that different people could end up with different conclusions given same plots.


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It seems like the Rayleigh Quotient, and you can refer to this article: The Rayleigh’s principle and the minimax principle for the eigenvalues of a self-adjoint matrix.


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