I am performing K means clustering on a gene expression dataset. I am aware of the fact that the Pearson correlation metric allows to group trends or patterns irrespective of their overall level of expression. I was wondering if the same concept stands for Covariance metric (I believe that the only difference between the two metrics is the fact that covariance returns unbounded values, while Pearson maps value in interval [-1,1])
k-means cluster analysis uses the variance and not the correlation. Many people "sphere" data prior to applying k-means (i.e., subtract the mean from each variable and then divide each variable by its standard deviation). This prevents variables with large variances from dominating the k-means solution.