Classification of the Entities Represented by Samples from Gaussian Distribution

Abstract

This paper aims to cluster entities which are described by a data matrix. Under the assumption of normality of observations contained in each table, each entity is represented by samples from Gaussian distribution, that is, a number of measurements in the data matrix, the sample mean vector, and the sample covariance. We propose a new distance based on Mahalanobis's discriminant score to measure the similarity between objects. The present study is thought to be an important and interesting topic of research not only in the quest for an adequate model of the data representation but also in the choice of the distance index between entities that would allow justifying the homogeneity of any observed classes.