Stochastic Programming for Selection Variables in Cluster Analysis

Abstract

Cluster analysis is one of the most important techniques in the exploratory data analysis; it is goal to discover a natural grouping in a set of observations without knowledge of any class labels.  Variable selection has been very important for a lot of research in several areas of application. The study suggested a stochastic programming approach which selects the most important variables in clustering a set of data. The study evaluates the performance of the stochastic programming suggested approach for selection variables in cluster analysis used numerical example. The suggested stochastic programming approach selects the most important variable in cluster analysis simultaneously and the results are satisfied.