Results 261 to 270 of about 797,470 (304)

Model-based cluster analysis

Pattern Recognition, 1993
Abstract The problem of dot clustering is studied from a model-based viewpoint. A set of “placement” processes is chosen, each of which associates a probability with each location in a discrete space; in other words, a placement is a probability mass function (pmf) on the space.
Saibal Banerjee, Azriel Rosenfeld
openaire   +1 more source

Model-Based Edge Clustering

Journal of Computational and Graphical Statistics, 2020
Relational data can be studied using network analytic techniques which define the network as a set of actors and a set of edges connecting these actors.
openaire   +1 more source

A model-based distance for clustering

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
A Riemannian distance is defined which is appropriate for clustering multivariate data. This distance requires that data is first fitted with a differentiable density model allowing the definition of an appropriate Riemannian metric. A tractable approximation is developed for the case of a Gaussian mixture model and the distance is tested on artificial
openaire   +3 more sources

Model-Based Clustering

2009
Finite mixture models are being commonly used in a wide range of applications in practice concerning density estimation and clustering. An attractive feature of this approach to clustering is that it provides a sound statistical framework in which to assess the important question of how many clusters are there in the data and their validity.
openaire   +4 more sources

Challenges in model‐based clustering

WIREs Computational Statistics, 2013
AbstractModel‐based clustering is an increasingly popular area of cluster analysis that relies on probabilistic description of data by means of finite mixture models. Mixture distributions prove to be a powerful technique for modeling heterogeneity in data.
openaire   +1 more source

Model-based clustering for longitudinal data

Computational Statistics & Data Analysis, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rolando De la Cruz-Mesía, Fernando A. Quintana, Guillermo Marshall   +2 more
openaire   +3 more sources

Probabilistic assessment of model-based clustering

Advances in Data Analysis and Classification, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xuwen Zhu, Volodymyr Melnykov
openaire   +2 more sources

Studying Complexity of Model-based Clustering

Communications in Statistics - Simulation and Computation, 2016
Cluster analysis is a popular statistics and computer science technique commonly used in various areas of research. In this article, we investigate factors that can influence clustering performance in the model-based clustering framework. The four factors considered are the level of overlap, number of clusters, number of dimensions, and sample size ...
Semhar Michael, Volodymyr Melnykov
openaire   +1 more source

Inference in model-based cluster analysis

Statistics and Computing, 1997
A new approach to cluster analysis has been introduced based on parsimonious geometric modelling of the within-group covariance matrices in a mixture of multivariate normal distributions, using hierarchical agglomeration and iterative relocation. It works well and is widely used via the MCLUST software available in S-PLUS and StatLib.
Halima Bensmail, Gilles Celeux, Adrian E. Raftery, Christian P. Robert   +3 more
openaire   +1 more source

Model-Based Clustering of Temporal Data

2013
This paper addresses the problem of temporal data clustering using a dynamic Gaussian mixture model whose means are considered as latent variables distributed according to random walks. Its final objective is to track the dynamic evolution of some critical railway components using data acquired through embedded sensors.
El Assaad, Hani, Same, Allou, Govaert, Gérard, Aknin, Patrice   +3 more
openaire   +2 more sources