Abstract In the last few years, need of preventing classification biases due to race, gender, social status, etc. has increased interest in designing fair clustering algorithms. The main idea is ensure that output a cluster algorithm not biased towards or against specific subgroups population. There growing specialized literature on this topic, dealing with problem numerical data bases. Neverth...

Abstract We propose a method for estimation in high-dimensional linear models with nominal categorical data. Our estimator, called SCOPE, fuses levels together by making their corresponding coefficients exactly equal. This is achieved using the minimax concave penalty on differences between order statistics of variable, thereby clustering coefficients. provide an algorithm exact and efficient c...