東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Some Contributions to Modern Mixture...

Wang, Yang,

Linked to FindBook

Google Book

Amazon

博客來

Some Contributions to Modern Mixture Modeling and Model-Based Clustering /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Some Contributions to Modern Mixture Modeling and Model-Based Clustering // Yang Wang.
Author:	Wang, Yang,
Description:	1 electronic resource (110 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28542563
ISBN:	9798538141777

Some Contributions to Modern Mixture Modeling and Model-Based Clustering /
Wang, Yang,

Some Contributions to Modern Mixture Modeling and Model-Based Clustering /Yang Wang. - 1 electronic resource (110 pages)

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Clustering analysis is a technique of recognizing groups of similar objects. Based on the finite mixture models, model-based clustering is one of the most popular methods due to its flexibility and interpretability in modeling heterogeneous data. In this background, the one-to-one correspondence between mixture components and groups is assumed. The clustering process can be viewed as the model estimation by using an optimization algorithm. The age of big data poses new challenges. Due to a potentially high number of parameters, finite mixture models are often at the risk of being overparameterized. The overparameterization in model-based clustering often results in mixture order underestimation. As a fast-growing field, developing simulation studies to validate the mixture models becomes another crucial topic. This thesis contributes to modern mixture modeling and model-based clustering, and mainly focuses on developing approaches for solving overparameterization issues in this context. In addition, algorithms for simulating various types of clusters are created, which can be utilized to evaluate and improve clustering techniques. For each of the chapters, the expectation-maximization (EM) algorithm of the proposed mixture is developed, the expressions for model parameter estimations are provided, and corresponding parsimonious procedures are proposed. The utilities of methodologies are tested on both synthetic and well-known classification datasets. The organization of the thesis is as follows. In the first chapter, a variable selection procedure is developed and applied in the matrix mixture modeling. The second chapter develops a novel mixture modeling approach called conditional mixture modeling and its corresponding parsimonious procedure. The third chapter provides an extension for simulating heterogeneous data for studying the systematic performance of clustering algorithms. Finally, the fourth chapter describes an R package cmbClust functionality developed for clustering multivariate data using the methodology proposed in chapter two.

English

ISBN: 9798538141777Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Modern mixture

Some Contributions to Modern Mixture Modeling and Model-Based Clustering /
LDR:03397nmm a22003733i 4500 001 2434648
005 20250923060935.5
006 m o d
007 cr|nu||||||||
008 261011s2021 miu||||||m |||||||eng d
020 $a 9798538141777
035 $a (MiAaPQD)AAI28542563
035 $a AAI28542563
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Wang, Yang, $e author. $3 3824700
245 1 0 $a Some Contributions to Modern Mixture Modeling and Model-Based Clustering / $c Yang Wang.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 1 electronic resource (110 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
500 $a Advisors: Melnykov, Volodymyr Committee members: Mugoya, George; Lee, Danhyang; Melnykov, Yana; Wang, Qin ; Zhu, Xuwen.
502 $b Ph.D. $c The University of Alabama $d 2021.
520 $a Clustering analysis is a technique of recognizing groups of similar objects. Based on the finite mixture models, model-based clustering is one of the most popular methods due to its flexibility and interpretability in modeling heterogeneous data. In this background, the one-to-one correspondence between mixture components and groups is assumed. The clustering process can be viewed as the model estimation by using an optimization algorithm. The age of big data poses new challenges. Due to a potentially high number of parameters, finite mixture models are often at the risk of being overparameterized. The overparameterization in model-based clustering often results in mixture order underestimation. As a fast-growing field, developing simulation studies to validate the mixture models becomes another crucial topic. This thesis contributes to modern mixture modeling and model-based clustering, and mainly focuses on developing approaches for solving overparameterization issues in this context. In addition, algorithms for simulating various types of clusters are created, which can be utilized to evaluate and improve clustering techniques. For each of the chapters, the expectation-maximization (EM) algorithm of the proposed mixture is developed, the expressions for model parameter estimations are provided, and corresponding parsimonious procedures are proposed. The utilities of methodologies are tested on both synthetic and well-known classification datasets. The organization of the thesis is as follows. In the first chapter, a variable selection procedure is developed and applied in the matrix mixture modeling. The second chapter develops a novel mixture modeling approach called conditional mixture modeling and its corresponding parsimonious procedure. The third chapter provides an extension for simulating heterogeneous data for studying the systematic performance of clustering algorithms. Finally, the fourth chapter describes an R package cmbClust functionality developed for clustering multivariate data using the methodology proposed in chapter two.
546 $a English
590 $a School code: 0004
650 4 $a Statistics. $3 517247
650 4 $a Variables. $3 3548259
650 4 $a Datasets. $3 3541416
650 4 $a Algorithms. $3 536374
650 4 $a Clustering. $3 3559215
653 $a Modern mixture
653 $a Clustering
690 $a 0463
710 2 $a The University of Alabama. $b Applied Statistics. $e degree granting institution. $3 3824701
720 1 $a Melnykov, Volodymyr $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 83-04B.
790 $a 0004
791 $a Ph.D.
792 $a 2021
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28542563