Does K-means assume normal distribution?
k-means assume the variance of the distribution of each attribute (variable) is spherical; all variables have the same variance; the prior probability for all k clusters are the same, i.e. each cluster has roughly equal number of observations; If any one of these 3 assumptions is violated, then k-means will fail.
Is K-means parametric or nonparametric?
Cluster means from the k-means algorithm are nonparametric estimators of principal points. A parametric k-means approach is introduced for estimating principal points by running the k-means algorithm on a very large simulated data set from a distribution whose parameters are estimated using maximum likelihood.
When does data fit a non normal distribution?
Sorted Data: Data collected from a normally distributed process can also fit a non-normal distribution if it represents just a sample / subset of the total output of the process. This happens when the collected data is sorted and then analyzed.
When is k-means robust to violations of normality?
My experience is that K-means, as with many traditional methods, is fairly robust to violations of normality when the magnitude of the noise is not large. In this sense, K-means differs significantly from, e.g., OLS regression assumptions that the errors be normally distributed (not the inputs).
How is k-means different from OLS regression?
In this sense, K-means differs significantly from, e.g., OLS regression assumptions that the errors be normally distributed (not the inputs). K-means does not have similar assumptions.
Is the sampling distribution of data in real life normal?
No data in real life is normal, a lot of them highly skewed, but that does NOT mean its sampling distribution is not normal. But many people do not know the difference so here’s a link to an animated explanation: http://onlinestatbook.com/stat_sim/sampling_dist/