Tutorial: Neutralizing Outliers in Any Dimension

It applies to problems such as clustering (finding centroids,) regression, measuring correlation or R-Squared, and many more. The focus here is on finding the point that minimizes the sum of the "distances" to n points in a d-dimensional space, called centroid or center, especially in the presence of outliers. Some simple stochastic processes can be simulated by first simulating random points (called centers) uniformly distributed in a rectangle, then, around each center, simulating a random number of points radially distributed around each center. In this case, the data set S consists of n 100 points randomly (uniformly) distributed on [0, 1] x [0, 1].