Maurizio Vichi
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View article: Bi-dendrograms for clustering the categories of a multivariate categorical data set
Bi-dendrograms for clustering the categories of a multivariate categorical data set Open
The clustering of categories in a multivariate categorical data set is investigated, where the problem separates into that of merging categories of the same variables (i.e., within-variable categories), and combining categories of differen…
View article: A novel clustering method with maximum number of ordered centroids and stable clusters for optimal ranking in a univariate setting
A novel clustering method with maximum number of ordered centroids and stable clusters for optimal ranking in a univariate setting Open
This paper proposes an innovative method to determine the optimal ranking of a set of univariate units in the maximum number of clusters with sortable centroids. Units within the identified clusters are considered equivalent, while units b…
View article: Clustering for ranking multivariate data by Linear Ordered Partitions
Clustering for ranking multivariate data by Linear Ordered Partitions Open
This paper explores the use of clustering to rank multivariate observations by linking ranking to clustering through the Linear Ordered Partition (LOP) concept. A LOP allows optimal clustering into ordered “ equivalence classes ”. In fact,…
View article: Uncovering socioeconomic disparities in European regions: a Tucker 3 clustering approach
Uncovering socioeconomic disparities in European regions: a Tucker 3 clustering approach Open
This study presents a multidimensional analysis of socioeconomic disparities across European regions between 2019 and 2023, focusing on key Sustainable Development Goal (SDG) indicators related to poverty (SDG 1), quality education (SDG 4)…
View article: Ultrametric Factor Analysis for Building Hierarchies of Reliable and Unidimensional Latent Concepts
Ultrametric Factor Analysis for Building Hierarchies of Reliable and Unidimensional Latent Concepts Open
This article introduces a novel methodology to model the hierarchical dependence structure of manifest variables (MVs). This is done by reconstructing their correlation matrix considering a hierarchy of latent factors which forms an ultram…
View article: Spherical Double K-Means: a co-clustering approach for text data analysis
Spherical Double K-Means: a co-clustering approach for text data analysis Open
In text analysis, Spherical K-means (SKM) is a specialized k-means clustering algorithm widely utilized for grouping documents represented in high-dimensional, sparse term-document matrices, often normalized using techniques like TF-IDF. R…
View article: Editorial for ADAC issue 2 of volume 18 (2024)
Editorial for ADAC issue 2 of volume 18 (2024) Open
View article: Mixture models for simultaneous classification and reduction of three-way data
Mixture models for simultaneous classification and reduction of three-way data Open
Finite mixture of Gaussians are often used to classify two- (units and variables) or three- (units, variables and occasions) way data. However, two issues arise: model complexity and capturing the true cluster structure. Indeed, a large nu…
View article: drclust: Simultaneous Clustering and (or) Dimensionality Reduction
drclust: Simultaneous Clustering and (or) Dimensionality Reduction Open
Methods for simultaneous clustering and dimensionality reduction such as: Double kmeans, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA.…
View article: Parsimonious consensus hierarchies, partitions and fuzzy partitioning of a set of hierarchies
Parsimonious consensus hierarchies, partitions and fuzzy partitioning of a set of hierarchies Open
Methodology is described for fitting a fuzzy partition and a parsimonious consensus hierarchy (ultrametric matrix) to a set of hierarchies of the same set of objects. A model defining a fuzzy partition of a set of hierarchical classificati…
View article: Parsimonious ultrametric Gaussian mixture models
Parsimonious ultrametric Gaussian mixture models Open
Gaussian mixture models represent a conceptually and mathematically elegant class of models for casting the density of a heterogeneous population where the observed data is collected from a population composed of a finite set of G homogene…
View article: Structural equation models for simultaneous modeling of air pollutants
Structural equation models for simultaneous modeling of air pollutants Open
This paper provides a new modeling for air pollution, simultaneously taking into account the six main pollutants (PM10 and PM2.5, Sulphate Dioxide, Nitrogen Dioxide, Carbon Monoxide, ground level Ozone concentrations) and their key determi…
View article: New methodologies to build an integrated global plankton database: the GreenSeas Analysis Framework
New methodologies to build an integrated global plankton database: the GreenSeas Analysis Framework Open
No abstracts are to be cited without prior reference to the author.In order to assess the current state of the marine planktonic ecosystem on an Atlantic basin scale it is necessary to provide a consistent framework through which historica…
View article: Consensus and fuzzy partition of dendrograms from a three-way dissimilarity array
Consensus and fuzzy partition of dendrograms from a three-way dissimilarity array Open
In cluster analysis, a problem often addressed is finding a consensus on a set of hierarchical classifications of the same set of objects, named primary hierarchies (dendrograms). A unique consensus of the primary hierarchies, called a sec…
View article: A model-based ultrametric composite indicator for studying waste management in Italian municipalities
A model-based ultrametric composite indicator for studying waste management in Italian municipalities Open
A Composite Indicator (CI) is a useful tool to synthesize information on a multidimensional phenomenon and make policy decisions. Multidimensional phenomena are often modeled by hierarchical latent structures that reconstruct relationships…
View article: Clustering Trajectories of a Three-Way Longitudinal Dataset
Clustering Trajectories of a Three-Way Longitudinal Dataset Open
Longitudinal data are widely used information for repeated observations of the same units over a period of time in order to investigate developmental trends across life span of units. Each object depicts, in the space of the features and o…
View article: Statistical Models and Methods for Data Science
Statistical Models and Methods for Data Science Open
View article: Hierarchical Means Clustering
Hierarchical Means Clustering Open
In the cluster analysis literature, there are several partitioning (non-hierarchical) methods for clustering multivariate objects based on model estimation. Distinct to these methods is the use of a system of n nested statistical models an…
View article: Hierarchical disjoint principal component analysis
Hierarchical disjoint principal component analysis Open
View article: Editorial for ADAC issue 3 of volume 16 (2022)
Editorial for ADAC issue 3 of volume 16 (2022) Open
View article: Gaussian mixture model with an extended ultrametric covariance structure
Gaussian mixture model with an extended ultrametric covariance structure Open
View article: Periodontal results of different therapeutic approaches (open vs. closed technique) and timing evaluation (< 2 year vs. > 2 year) of palatal impacted canines: a systematic review
Periodontal results of different therapeutic approaches (open vs. closed technique) and timing evaluation (< 2 year vs. > 2 year) of palatal impacted canines: a systematic review Open
View article: Second-Order Disjoint Factor Analysis
Second-Order Disjoint Factor Analysis Open
Hierarchical models are often considered to measure latent concepts defining nested sets of manifest variables. Therefore, by supposing a hierarchical relationship among manifest variables, the general latent concept can be represented by …
View article: Editorial for ADAC issue 2 of volume 15 (2021)
Editorial for ADAC issue 2 of volume 15 (2021) Open
View article: Construction of an Immigrant Integration Composite Indicator through the Partial Least Squares Structural Equation Model K-Means
Construction of an Immigrant Integration Composite Indicator through the Partial Least Squares Structural Equation Model K-Means Open
View article: An empirical comparison of two approaches for CDPCA in high-dimensional data
An empirical comparison of two approaches for CDPCA in high-dimensional data Open
View article: Building Well-Being Composite Indicator for Micro-Territorial Areas Through PLS-SEM and K-Means Approach
Building Well-Being Composite Indicator for Micro-Territorial Areas Through PLS-SEM and K-Means Approach Open
In the analysis of the difference in the distribution and profiles of the equitable and sustainable well-being, the territorial dimension is a fundamental reading-key for local policies since it allows the areas of advantage or relative de…
View article: Finding groups in structural equation modeling through the partial least squares algorithm
Finding groups in structural equation modeling through the partial least squares algorithm Open
View article: Trusted smart statistics: The challenge of extracting usable aggregate information from new data sources
Trusted smart statistics: The challenge of extracting usable aggregate information from new data sources Open
Recent years have seen dramatic changes in sources of data, amounts of data, availability of data, frequency of data, and types of data. Along with advances in data analytic technology these changes have opened up huge possibilities for im…
View article: Clustering and dimension reduction for mixed variables
Clustering and dimension reduction for mixed variables Open