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Model-Based Clustering, Classification, and Density Estimation Using mclust in R

2023-04-20 Mathematics
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Release : 2023-04-20
Genre : Mathematics
Kind : eBook
Book Rating : 346/5 ( reviews)

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Book Synopsis Model-Based Clustering, Classification, and Density Estimation Using mclust in R by : Luca Scrucca

Download or read book Model-Based Clustering, Classification, and Density Estimation Using mclust in R written by Luca Scrucca. This book was released on 2023-04-20. Available in PDF, EPUB and Kindle. Book excerpt: Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models. Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for mclust, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.

Finite Mixture Models

2004-03-22 Mathematics
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Release : 2004-03-22
Genre : Mathematics
Kind : eBook
Book Rating : 06X/5 ( reviews)

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Book Synopsis Finite Mixture Models by : Geoffrey McLachlan

Download or read book Finite Mixture Models written by Geoffrey McLachlan. This book was released on 2004-03-22. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.

MCLUST: Software for Model-Based Clustering, Density Estimation and Discriminant Analysis

2002
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Release : 2002
Genre :
Kind : eBook
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Book Synopsis MCLUST: Software for Model-Based Clustering, Density Estimation and Discriminant Analysis by :

Download or read book MCLUST: Software for Model-Based Clustering, Density Estimation and Discriminant Analysis written by . This book was released on 2002. Available in PDF, EPUB and Kindle. Book excerpt: MCLUST is a software package for model-based clustering, density estimation and discriminant analysis interfaced to the S-PLUS commercial software. It implements parameterized Gaussian hierarchical clustering algorithms and the EM algorithm for parameterized Gaussian mixture models with the possible addition of a Poisson noise term. Also included are functions that combine hierarchical clustering, EM and the Bayesian Information Criterion (BIC) in comprehensive strategies for clustering, density estimation, and discriminant analysis. MCLUST provides functionality for displaying and visualizing clustering and classification results. A web page with related links can be found at http;//www.stat.washington.edu/mclust.

Model-Based Clustering and Classification for Data Science

2019-07-25 Business & Economics
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Release : 2019-07-25
Genre : Business & Economics
Kind : eBook
Book Rating : 20X/5 ( reviews)

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Book Synopsis Model-Based Clustering and Classification for Data Science by : Charles Bouveyron

Download or read book Model-Based Clustering and Classification for Data Science written by Charles Bouveyron. This book was released on 2019-07-25. Available in PDF, EPUB and Kindle. Book excerpt: Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.

MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering

2006
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Release : 2006
Genre :
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Book Synopsis MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering by :

Download or read book MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering written by . This book was released on 2006. Available in PDF, EPUB and Kindle. Book excerpt: MCLUST is a contributed R package for normal mixture modeling and model-based clustering. It provides functions for parameter estimation via the EM algorithm for normal mixture models with a variety of covariance structures, and functions for simulation from these models. Also included are functions that combine model-based hierarchical clustering, EM for mixture estimation and the Bayesian Information Criterion (BIC) in comprehensive strategies for clustering, density estimation and discriminant analysis. There is additional functionality for displaying and visualizing the models along with clustering and classification results. A number of features of the software have been changed in this version, and the functionality has been expanded to include regularization for normal mixture models via a Bayesian prior. A web page with related links including license information can be found at http://www.stat.washington.edu/mclust.

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