I'm trying to calculate the densest cluster of values along an x axis. I recently discovered Kernel Density Estimation, before hand I was just simply averaging all the values along the x axis to try and determine the densest point. Considering mean finds the central tendency which is also influenced by the densest cluster of values, how would kernel density estimation and mean differ in finding the densest point?
When I looked "kernel density estimation" and "mean" up as keywords, I couldn't find any results discussing both topics. So I take it this is a very uneducated question. For that I apologise in advance. From what I understand, kde's primary focus is on probability while of course mean is just to determine general central tendencies. But it definitely intrigues me that they output similar estimations of what appear to be the densest point of a dataset. Perhaps outliers play a role here?