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Information Journal Paper

Title

ON MODEL-BASED CLUSTERING, CLASSIFICATION, AND DISCRIMINANT ANALYSIS

Pages

  181-199

Abstract

 The use of MIXTURE MODELS for CLUSTERING and CLASSIFICATION has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based CLUSTERING, CLASSIFICATION, and DISCRIMINANT ANALYSIS, with particular attention being paid to two techniques that can be implemented using respective R packages. PARAMETER ESTIMATION and MODEL SELECTION are also discussed. The paper concludes with a summary, discussion, and some thoughts on future work.

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    APA: Copy

    MCNICHOLAS, PAUL D.. (2011). ON MODEL-BASED CLUSTERING, CLASSIFICATION, AND DISCRIMINANT ANALYSIS. JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS), 10(2), 181-199. SID. https://sid.ir/paper/117833/en

    Vancouver: Copy

    MCNICHOLAS PAUL D.. ON MODEL-BASED CLUSTERING, CLASSIFICATION, AND DISCRIMINANT ANALYSIS. JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS)[Internet]. 2011;10(2):181-199. Available from: https://sid.ir/paper/117833/en

    IEEE: Copy

    PAUL D. MCNICHOLAS, “ON MODEL-BASED CLUSTERING, CLASSIFICATION, AND DISCRIMINANT ANALYSIS,” JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS), vol. 10, no. 2, pp. 181–199, 2011, [Online]. Available: https://sid.ir/paper/117833/en

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