Other Mixture Models for Categorical Data

2003 ◽  
pp. 538-575 ◽  
Keyword(s):  
Mixture Models ◽  
Categorical Data

scholarly journals Marginalizing pattern-mixture models for categorical data subject to monotone missingness

Metrika ◽  
2008 ◽  
Vol 69 (2-3) ◽  
pp. 305-336 ◽  
Author(s):  
Cristina Sotto ◽  
Caroline Beunckens ◽  
Geert Molenberghs ◽  
Ivy Jansen ◽  
Geert Verbeke
Keyword(s):  
Mixture Models ◽  
Categorical Data ◽  
Data Subject

scholarly journals A Complete Classification and Clustering Model to Account for Continuous and Categorical Data in Presence of Missing Values and Outliers †

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 23
Author(s):  
Guillaume Revillon ◽  
Ali Mohammad-Djafari
Keyword(s):  
Missing Data ◽  
Mixture Models ◽  
Categorical Data ◽  
Missing Values ◽  
Complete Classification ◽  
Electronic Warfare ◽  
Variational Bayesian Inference ◽  
Clustering Model ◽  
Mixture Of Normal Distributions ◽  
Classification And Clustering

Classification and clustering problems are closely connected with pattern recognition where many general algorithms have been developed and used in various fields. Depending on the complexity of patterns in data, classification and clustering procedures should take into consideration both continuous and categorical data which can be partially missing and erroneous due to mismeasurements and human errors. However, most algorithms cannot handle missing data and imputation methods are required to generate data to use them. Hence, the main objective of this work is to define a classification and clustering framework that handles both outliers and missing values. Here, an approach based on mixture models is preferred since mixture models provide a mathematically based, flexible and meaningful framework for the wide variety of classification and clustering requirements. More precisely, a scale mixture of Normal distributions is updated to handle outliers and missing data issues for any types of data. Then a variational Bayesian inference is used to find approximate posterior distributions of parameters and to provide a lower bound on the model log evidence used as a criterion for selecting the number of clusters. Eventually, experiments are carried out to exhibit the effectiveness of the proposed model through an application in Electronic Warfare.

scholarly journals Evolutionary clustering for categorical data using parametric links among multinomial mixture models

2017 ◽  
Vol 3 ◽  
pp. 141-159 ◽  
Author(s):  
Md. Abul Hasnat ◽  
Julien Velcin ◽  
Stephane Bonnevay ◽  
Julien Jacques
Keyword(s):  
Mixture Models ◽  
Categorical Data ◽  
Evolutionary Clustering

Post Hoc Investigation of Factor Mixture Models

2007 ◽  
Author(s):  
Danielle L. Cisler ◽  
Gitta H. Lubke
Keyword(s):  
Mixture Models ◽  
Post Hoc
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