Model Based Clustering Classification And Density Estimation Using Mclust In R

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

Author : Luca Scrucca
Publisher : CRC Press
ISBN 13 : 1000868346
Total Pages : 269 pages
Book Rating : 4.0/5 (8 download)

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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 and published by CRC Press. This book was released on 2023-04-20 with total page 269 pages. 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

Author : Geoffrey McLachlan
Publisher : John Wiley & Sons
ISBN 13 : 047165406X
Total Pages : 419 pages
Book Rating : 4.4/5 (716 download)

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Book Synopsis Finite Mixture Models by : Geoffrey McLachlan

Download or read book Finite Mixture Models written by Geoffrey McLachlan and published by John Wiley & Sons. This book was released on 2004-03-22 with total page 419 pages. 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.

Model-Based Clustering and Classification for Data Science

Author : Charles Bouveyron
Publisher : Cambridge University Press
ISBN 13 : 1108640591
Total Pages : 447 pages
Book Rating : 4.1/5 (86 download)

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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 and published by Cambridge University Press. This book was released on 2019-07-25 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Model-Based Clustering and Classification for Data Science

Author : Charles Bouveyron
Publisher : Cambridge University Press
ISBN 13 : 110849420X
Total Pages : 446 pages
Book Rating : 4.1/5 (84 download)

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

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

Author :
Publisher :
ISBN 13 :
Total Pages : 51 pages
Book Rating : 4.:/5 (227 download)

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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 and published by . This book was released on 2002 with total page 51 pages. 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.

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

Author :
Publisher :
ISBN 13 :
Total Pages : 51 pages
Book Rating : 4.:/5 (227 download)

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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 and published by . This book was released on 2006 with total page 51 pages. 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.

Hands-On Machine Learning with R

Author : Brad Boehmke
Publisher : CRC Press
ISBN 13 : 1000730433
Total Pages : 374 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Hands-On Machine Learning with R by : Brad Boehmke

Download or read book Hands-On Machine Learning with R written by Brad Boehmke and published by CRC Press. This book was released on 2019-11-07 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Mixture Model-Based Classification

Author : Paul D. McNicholas
Publisher : CRC Press
ISBN 13 : 1482225670
Total Pages : 212 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Mixture Model-Based Classification by : Paul D. McNicholas

Download or read book Mixture Model-Based Classification written by Paul D. McNicholas and published by CRC Press. This book was released on 2016-10-04 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some time. The discussion of mixtures with heavy tails and asymmetric distributions will place this text as the authoritative, modern reference in the mixture modeling literature." (Douglas Steinley, University of Missouri) Mixture Model-Based Classification is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively, including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered, from mixtures with components that parameterize skewness and/or concentration, right up to mixtures of multiple scaled distributions. Several other important topics are considered, including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a cluster Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. His research focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has published extensively within the field. He is an associate editor for several journals and has served as a guest editor for a number of special issues on mixture models.

An Introduction to Clustering with R

Author : Paolo Giordani
Publisher : Springer Nature
ISBN 13 : 9811305536
Total Pages : 340 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis An Introduction to Clustering with R by : Paolo Giordani

Download or read book An Introduction to Clustering with R written by Paolo Giordani and published by Springer Nature. This book was released on 2020-08-27 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.

Clustering and Classification

Author : Phipps Arabie
Publisher : World Scientific
ISBN 13 : 9789810212872
Total Pages : 508 pages
Book Rating : 4.2/5 (128 download)

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Book Synopsis Clustering and Classification by : Phipps Arabie

Download or read book Clustering and Classification written by Phipps Arabie and published by World Scientific. This book was released on 1996 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review of the Japanese-language results on clustering, review of the Russian-language results on clustering and multidimensional scaling, practical advances, and significance tests.

Spatial Data Mining

Author : Deren Li
Publisher : Springer
ISBN 13 : 3662485389
Total Pages : 329 pages
Book Rating : 4.6/5 (624 download)

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Book Synopsis Spatial Data Mining by : Deren Li

Download or read book Spatial Data Mining written by Deren Li and published by Springer. This book was released on 2016-03-23 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: · This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project ‘the Belt and Road Initiatives’.

Data Science for Mathematicians

Author : Nathan Carter
Publisher : CRC Press
ISBN 13 : 0429675682
Total Pages : 545 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Data Science for Mathematicians by : Nathan Carter

Download or read book Data Science for Mathematicians written by Nathan Carter and published by CRC Press. This book was released on 2020-09-15 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.

Applied Compositional Data Analysis

Author : Peter Filzmoser
Publisher : Springer
ISBN 13 : 3319964224
Total Pages : 280 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Applied Compositional Data Analysis by : Peter Filzmoser

Download or read book Applied Compositional Data Analysis written by Peter Filzmoser and published by Springer. This book was released on 2018-11-03 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.

Cluster Analysis

Author : Brian S. Everitt
Publisher :
ISBN 13 :
Total Pages : 122 pages
Book Rating : 4.:/5 (878 download)

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Book Synopsis Cluster Analysis by : Brian S. Everitt

Download or read book Cluster Analysis written by Brian S. Everitt and published by . This book was released on 1977 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Multivariate Models and Multivariate Dependence Concepts

Author : Harry Joe
Publisher : CRC Press
ISBN 13 : 9780412073311
Total Pages : 422 pages
Book Rating : 4.0/5 (733 download)

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Book Synopsis Multivariate Models and Multivariate Dependence Concepts by : Harry Joe

Download or read book Multivariate Models and Multivariate Dependence Concepts written by Harry Joe and published by CRC Press. This book was released on 1997-05-01 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book on multivariate models, statistical inference, and data analysis contains deep coverage of multivariate non-normal distributions for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and unsolved problems.

Matrix Variate Distributions

Author : A K Gupta
Publisher : CRC Press
ISBN 13 : 1351433008
Total Pages : 382 pages
Book Rating : 4.3/5 (514 download)

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Book Synopsis Matrix Variate Distributions by : A K Gupta

Download or read book Matrix Variate Distributions written by A K Gupta and published by CRC Press. This book was released on 2018-05-02 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Useful in physics, economics, psychology, and other fields, random matrices play an important role in the study of multivariate statistical methods. Until now, however, most of the material on random matrices could only be found scattered in various statistical journals. Matrix Variate Distributions gathers and systematically presents most of the recent developments in continuous matrix variate distribution theory and includes new results. After a review of the essential background material, the authors investigate the range of matrix variate distributions, including: matrix variate normal distribution Wishart distribution Matrix variate t-distribution Matrix variate beta distribution F-distribution Matrix variate Dirichlet distribution Matrix quadratic forms With its inclusion of new results, Matrix Variate Distributions promises to stimulate further research and help advance the field of multivariate statistical analysis.

An Introduction to Applied Multivariate Analysis with R

Author : Brian Everitt
Publisher : Springer Science & Business Media
ISBN 13 : 1441996508
Total Pages : 284 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis An Introduction to Applied Multivariate Analysis with R by : Brian Everitt

Download or read book An Introduction to Applied Multivariate Analysis with R written by Brian Everitt and published by Springer Science & Business Media. This book was released on 2011-04-23 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.