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

Bibliographic Details
Title: Model-Based Clustering, Classification, and Density Estimation Using Mclust in R
Description: 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.
Authors: Luca Scrucca, Chris Fraley, T. Brendan Murphy, Adrian E. Raftery
Resource Type: eBook.
Subjects: Estimation theory--Data processing, R (Computer program language), Cluster analysis--Data processing, Gaussian distribution--Data processing
Categories: MATHEMATICS / Probability & Statistics / Regression Analysis, BUSINESS & ECONOMICS / Statistics, COMPUTERS / Machine Theory, MATHEMATICS / Probability & Statistics / General
Database: eBook Index
FullText Text:
  Availability: 0
Header DbId: edsebk
DbLabel: eBook Index
An: 3581426
RelevancyScore: 969
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 968.509704589844
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Model-Based Clustering, Classification, and Density Estimation Using Mclust in R
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Luca+Scrucca%22">Luca Scrucca</searchLink><br /><searchLink fieldCode="AR" term="%22Chris+Fraley%22">Chris Fraley</searchLink><br /><searchLink fieldCode="AR" term="%22T%2E+Brendan+Murphy%22">T. Brendan Murphy</searchLink><br /><searchLink fieldCode="AR" term="%22Adrian+E%2E+Raftery%22">Adrian E. Raftery</searchLink>
– Name: TypePub
  Label: Resource Type
  Group: TypPub
  Data: eBook.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Estimation+theory--Data+processing%22">Estimation theory--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22R+%28Computer+program+language%29%22">R (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis--Data+processing%22">Cluster analysis--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+distribution--Data+processing%22">Gaussian distribution--Data processing</searchLink>
– Name: SubjectBISAC
  Label: Categories
  Group: Su
  Data: <searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+Regression+Analysis%22">MATHEMATICS / Probability & Statistics / Regression Analysis</searchLink><br /><searchLink fieldCode="ZK" term="%22BUSINESS+%26+ECONOMICS+%2F+Statistics%22">BUSINESS & ECONOMICS / Statistics</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Machine+Theory%22">COMPUTERS / Machine Theory</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink>
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3581426
RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 519.5302855133
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Estimation theory--Data processing
        Type: general
      – SubjectFull: R (Computer program language)
        Type: general
      – SubjectFull: Cluster analysis--Data processing
        Type: general
      – SubjectFull: Gaussian distribution--Data processing
        Type: general
    Titles:
      – TitleFull: Model-Based Clustering, Classification, and Density Estimation Using Mclust in R
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Luca Scrucca
      – PersonEntity:
          Name:
            NameFull: Chris Fraley
      – PersonEntity:
          Name:
            NameFull: T. Brendan Murphy
      – PersonEntity:
          Name:
            NameFull: Adrian E. Raftery
      – PersonEntity:
          Name:
            NameFull: Luca Scrucca
      – PersonEntity:
          Name:
            NameFull: Chris Fraley
      – PersonEntity:
          Name:
            NameFull: T. Brendan Murphy
      – PersonEntity:
          Name:
            NameFull: Adrian E. Raftery
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
            – D: 11
              M: 08
              Type: profile
              Y: 2023
          Identifiers:
            – Type: isbn-print
              Value: 9781032234953
            – Type: isbn-print
              Value: 9781032234960
            – Type: isbn-electronic
              Value: 9781000868340
            – Type: isbn-electronic
              Value: 9781000868371
            – Type: isbn-electronic
              Value: 9781003277965
          Titles:
            – TitleFull: Model-Based Clustering, Classification, and Density Estimation Using Mclust in R
              Type: main
ResultId 1