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Published in:
2019 | OriginalPaper | Chapter
A Parametric Version of Probabilistic Distance Clustering
Authors : Christopher Rainey, Cristina Tortora, Francesco Palumbo
Published in: Statistical Learning of Complex Data
Publisher: Springer International Publishing
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Abstract
Probabilistic distance (PD) clustering method grounds on the basic assumption that the product between the probability of the unit belonging to a cluster and the distance between the unit and the cluster center is constant, for each statistical unit. This constant is a measure of the classificability of the point, and the sum of the constant over units is referred to as the joint distance function (JDF). The parameters that minimize the JDF maximize the classificability of the units. The goal of this paper is to introduce a new distance measure based on a probability density function, specifically, we use the multivariate Gaussian and Student-t distributions. We show using two simulated data sets that the use of a distance based on these two density functions improves the performance of PD clustering.