Variable selection methods for model-based clustering
Abstract Model-based clustering is a popular approach for clustering multivariate data which
has seen applications in numerous fields. Nowadays, high-dimensional data are more and …
has seen applications in numerous fields. Nowadays, high-dimensional data are more and …
A review of uncertainty modelling techniques for probabilistic stability analysis of renewable-rich power systems
In pursuit of identifying the most accurate and efficient uncertainty modelling (UM)
techniques, this paper provides an extensive review and classification of the available UM …
techniques, this paper provides an extensive review and classification of the available UM …
Model-based clustering
PD McNicholas - Journal of Classification, 2016 - Springer
The notion of defining a cluster as a component in a mixture model was put forth by
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …
C-vine copula mixture model for clustering of residential electrical load pattern data
The ongoing deployment of residential smart meters in numerous jurisdictions has led to an
influx of electricity consumption data. This information presents a valuable opportunity to …
influx of electricity consumption data. This information presents a valuable opportunity to …
Model based clustering for mixed data: clustMD
D McParland, IC Gormley - Advances in Data Analysis and Classification, 2016 - Springer
A model based clustering procedure for data of mixed type, clustMD, is developed using a
latent variable model. It is proposed that a latent variable, following a mixture of Gaussian …
latent variable model. It is proposed that a latent variable, following a mixture of Gaussian …
Distance metrics and clustering methods for mixed‐type data
AH Foss, M Markatou, B Ray - International Statistical Review, 2019 - Wiley Online Library
In spite of the abundance of clustering techniques and algorithms, clustering mixed interval
(continuous) and categorical (nominal and/or ordinal) scale data remain a challenging …
(continuous) and categorical (nominal and/or ordinal) scale data remain a challenging …
A generalized multi-aspect distance metric for mixed-type data clustering
Distance calculation is straightforward when working with pure categorical or pure numerical
data sets. Defining a unified distance to improve the clustering performance for a mixed data …
data sets. Defining a unified distance to improve the clustering performance for a mixed data …
Fully automatic lesion localization and characterization: Application to brain tumors using multiparametric quantitative MRI data
When analyzing brain tumors, two tasks are intrinsically linked, spatial localization, and
physiological characterization of the lesioned tissues. Automated data-driven solutions exist …
physiological characterization of the lesioned tissues. Automated data-driven solutions exist …
Football tracking data: a copula-based hidden Markov model for classification of tactics in football
Driven by recent advances in technology, tracking devices allow to collect high-frequency
data on the position of players in (association) football matches and in many other sports …
data on the position of players in (association) football matches and in many other sports …
An overview of skew distributions in model-based clustering
The literature on non-normal model-based clustering has continued to grow in recent years.
The non-normal models often take the form of a mixture of component densities that offer a …
The non-normal models often take the form of a mixture of component densities that offer a …