Abstract
Many relevant multidimensional phenomena are defined by nested latent concepts,
which can be represented by a tree-structure supposing a hierarchical relationship
among manifest variables. The root of the tree is a general concept which includes
more specific ones. The aim of the paper is to reconstruct an observed data correlation
matrix of manifest variables through an ultrametric correlation matrix which is able to
pinpoint the hierarchical nature of the phenomenon under study. With this scope, we
introduce a novel model which detects consistent latent concepts and their relationships
starting from the observed correlation matrix
which can be represented by a tree-structure supposing a hierarchical relationship
among manifest variables. The root of the tree is a general concept which includes
more specific ones. The aim of the paper is to reconstruct an observed data correlation
matrix of manifest variables through an ultrametric correlation matrix which is able to
pinpoint the hierarchical nature of the phenomenon under study. With this scope, we
introduce a novel model which detects consistent latent concepts and their relationships
starting from the observed correlation matrix
| Original language | English |
|---|---|
| Pages (from-to) | 837-853 |
| Journal | Advances in Data Analysis and Classification |
| Volume | 14 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 28 May 2020 |
| Externally published | Yes |