Results 291 to 300 of about 1,500,611 (328)

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

Copula Functions in Model Based Clustering

2006
Model based clustering is common approach used in cluster analysis. Here each cluster is characterized by some kind of model, for example — multivariate distribution, regression, principal component etc. One of the most well known approaches in model based clustering is the one proposed by Banfield and Raftery (1993), where each class is described by ...
Krzysztof Jajuga, Daniel Papla
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

Model-based clustered-dot screening

SPIE Proceedings, 2006
I propose a halftone screen design method based on a human visual system model and the characteristics of the electro-photographic (EP) printer engine. Generally, screen design methods based on human visual models produce dispersed-dot type screens while design methods considering EP printer characteristics generate clustered-dot type screens.
openaire   +1 more source

Visualization of Model-Based Clustering Structures

2009
Model-based clustering based on a finite mixture of Gaussian components is an effective method for looking for groups of observations in a dataset. In this paper we propose a dimension reduction method, called MCLUSTSIR, which is able to show clustering structures depending on the selected Gaussian mixture model.
openaire   +3 more sources

A unified framework for model-based clustering

J. Mach. Learn. Res.
Summary: Model-based clustering techniques have been widely used and have shown promising results in many applications involving complex data. This paper presents a unified framework for probabilistic model-based clustering based on a bipartite graph view of data and models that highlights the commonalities and differences among existing model-based ...
Shi Zhong 0001, Joydeep Ghosh
openaire   +2 more sources

TWC-EL: A multivariate prediction model by the fusion of three-way clustering and ensemble learning

Information Fusion, 2023
Jianming Zhan, Wei-Ping Ding
exaly  

Transfer-Learning-Based Gaussian Mixture Model for Distributed Clustering

IEEE Transactions on Cybernetics, 2023
Rongrong Wang, Shi-Yuan Han, Zhou Jin
exaly