Efficiency of Cluster Validity Indexes in Fuzzy Clusterwise Generalized Structured Component Analysis

Ryoo, Ji Hoon; Park, Seohee; Kim, Seong‐Eun; Ryoo, Hyun Suk

doi:10.3390/sym12091514

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…IG-GSCA currently estimates parameters by aggregating the data across observations under the implicit assumption that all observations come from a single homogenous population. In some cases, however, it may be more reasonable to assume that observations are drawn from (unknown) heterogeneous subgroups in the population, which exhibit different path-analytic relationships among observed variables and components [98][99][100]. Thus, future work is needed to simultaneously combine IG-GSCA with cluster analysis to capture such cluster-level heterogeneity, inspired by the development of fuzzy clusterwise GSCA [98].…”

Section: Plos Onementioning

confidence: 99%

A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

et al. 2021

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With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions’ variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.

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Section: Plos Onementioning

confidence: 99%

A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

et al. 2021

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“…On the one hand, clustering algorithms are based on unsupervised learning and are vital elements of machine learning in general. On the other hand, as a soft clustering algorithm, the usefulness of the fuzzy clustering algorithm has been confirmed in existing work [28]. However, in the recent research and application of fuzzy clustering [29,30], the algorithm has mainly been used as a classification technique.…”

Section: Imbalanced Learningmentioning

confidence: 97%

Class-Imbalanced Voice Pathology Detection and Classification Using Fuzzy Cluster Oversampling Method

Fan

Zhou

et al. 2021

Applied Sciences

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The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different types of pathological voice samples in the MEEI database. This study aimed to develop a VPD system that uses the fuzzy clustering synthetic minority oversampling technique algorithm (FC-SMOTE) to automatically detect and classify four types of pathological voices in a multi-class imbalanced database. The proposed FC-SMOTE algorithm processes the initial class-imbalanced dataset. A set of machine learning models was evaluated and validated using the resulting class-balanced dataset as an input. The effectiveness of the VPD system with FC-SMOTE was further verified by an external validation set and another pathological voice database (Saarbruecken Voice Database (SVD)). The experimental results show that, in the multi-classification of pathological voice for the class-imbalanced dataset, the method we propose can significantly improve the diagnostic accuracy. Meanwhile, FC-SMOTE outperforms the traditional imbalanced data oversampling algorithms, and it is preferred for imbalanced voice diagnosis in practical applications.

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“…Such a role of examining the effects of other variables on the LCA was replaced with GSCA in the fuzzy clusterwise GSCA that is a statistical tool of fitting various component-based structural equation models into data [9,10]. In addition, Ryoo et al [11] provided more indexes that can be utilized in identifying homogeneous subgroups and the procedure of enumerating the number of clusters, which is out of the scope of this manuscript. The method of fuzzy clusterwise GSCA will be described in Section 2.…”

Section: Introduction 1motivationmentioning

confidence: 99%

gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

Ryoo¹,

Park²,

Kim³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Clustering analysis identifying unknown heterogenous subgroups of a population (or a sample) has become increasingly popular along with the popularity of machine learning techniques. Although there are many software packages running clustering analysis, there is a lack of packages conducting clustering analysis within a structural equation modeling framework. The package, gscaLCA which is implemented in the R statistical computing environment, was developed for conducting clustering analysis and has been extended to a latent variable modeling. More specifically, by applying both fuzzy clustering (FC) algorithm and generalized structured component analysis (GSCA), the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities, which is applicable in mixture modeling such as latent class analysis in statistics. As a hybrid model between data clustering in classifications and model-based mixture modeling approach, fuzzy clusterwise GSCA, denoted as gscaLCA, encompasses many advantages from both methods: (1) soft partitioning from FC and (2) efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA. The main function, gscaLCA, works for both binary and ordered categorical variables. In addition, gscaLCA can be used for latent class regression as well. Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA. This paper contributes to providing a methodological tool, gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.

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Efficiency of Cluster Validity Indexes in Fuzzy Clusterwise Generalized Structured Component Analysis

Cited by 6 publications

References 26 publications

A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

Class-Imbalanced Voice Pathology Detection and Classification Using Fuzzy Cluster Oversampling Method

gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

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