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

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1514
Author(s):  
Ji Hoon Ryoo ◽  
Seohee Park ◽  
Seongeun Kim ◽  
Hyun Suk Ryoo
Keyword(s):  
Statistical Model ◽  
Fuzzy Clustering ◽  
Structural Equation ◽  
Component Analysis ◽  
Optimal Number ◽  
Equation Modeling ◽  
Cluster Validity ◽  
Generalized Structured Component Analysis ◽  
Validation Measure ◽  
Fit Indexes

Fuzzy clustering has been broadly applied to classify data into K clusters by assigning membership probabilities of each data point close to K centroids. Such a function has been applied into characterizing the clusters associated with a statistical model such as structural equation modeling. The characteristics identified by the statistical model further define the clusters as heterogeneous groups selected from a population. Recently, such statistical model has been formulated as fuzzy clusterwise generalized structured component analysis (fuzzy clusterwise GSCA). The same as in fuzzy clustering, the clusters are enumerated to infer the population and its parameters within the fuzzy clusterwise GSCA. However, the identification of clusters in fuzzy clustering is a difficult task because of the data-dependence of classification indexes, which is known as a cluster validity problem. We examined the cluster validity problem within the fuzzy clusterwise GSCA framework and proposed a new criterion for selecting the most optimal number of clusters using both fit indexes of the GSCA and the fuzzy validity indexes in fuzzy clustering. The criterion, named the FIT-FHV method combining a fit index, FIT, from GSCA and a cluster validation measure, FHV, from fuzzy clustering, performed better than any other indices used in fuzzy clusterwise GSCA.

scholarly journals Cutoff criteria for overall model fit indexes in generalized structured component analysis

2020 ◽  
Vol 8 (4) ◽  
pp. 189-202
Author(s):  
Gyeongcheol Cho ◽  
Heungsun Hwang ◽  
Marko Sarstedt ◽  
Christian M. Ringle
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Component Analysis ◽  
Model Fit ◽  
Equation Modeling ◽  
Generalized Structured Component Analysis ◽  
Root Mean Square Residual ◽  
Fit Indexes ◽  
Cutoff Criteria

AbstractGeneralized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.

scholarly journals ANALISIS DERAJAT KESEHATAN MASYARAKAT PROVINSI BALI DENGAN MENGGUNAKAN METODE GENERALIZED STRUCTURED COMPONENT ANALYSIS (GSCA)

2013 ◽  
Vol 2 (2) ◽  
pp. 54
Author(s):  
PUTU NOPITA PURNAMA NINGSIH ◽  
KETUT JAYANEGARA ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Structural Equation Modeling ◽  
Health Status ◽  
Health Service ◽  
Structural Equation ◽  
Component Analysis ◽  
Equation Modeling ◽  
Multivariate Normal ◽  
Behavioral Economic ◽  
Generalized Structured Component Analysis ◽  
The Relationship

The aim of this research is to determine the relationship between environmental, behavioral, health services, education, and economic variables to health status in the Province of Bali. These variables are constructs (latents ) that can not be measured directly by observation. If there was a relationship between latent and its indicators, it is recomended to use Structural Equation Modeling (SEM). In this research we used variance-based SEM i.e. Generalized Structured Component Analysis (GSCA). This method not based on many assumptions such as the data does not have a multivariate normal distribution, the sample size does is not necessary large. Moreover, GSCA provides by overall goodness-fit of the model. The result of this research indicates that the environmental, behavioral, economic and educational variable influenced health status, but health service does not significantly affect the health status; economic does not significantly affect the environment; and education does not significantly affect the behavior. The result of the FIT value ?0.450 and the AFIT value 0.429 showed that overall model in this research is not good enough because of both of these values are under 0.50.

An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis.

2020 ◽  
Author(s):  
Heungsun Hwang ◽  
Gyeongcheol Cho ◽  
Kwanghee Jung ◽  
Carl F. Falk ◽  
Jessica Kay Flake ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Component Analysis ◽  
Equation Modeling ◽  
Generalized Structured Component Analysis

scholarly journals STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS ON THE RELATIONSHIP BETWEEN RENUMERATION AND MOTIVATION ON EMPLOYEE PERFORMANCE AT UIN SUNAN KALIJAGA YOGYAKARTA

2020 ◽  
Vol 13 (2) ◽  
pp. 136-148
Author(s):  
Epha Diana Supandi
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Work Motivation ◽  
Positive Impact ◽  
Employee Performance ◽  
Component Analysis ◽  
Equation Modeling ◽  
Multivariate Statistical ◽  
Generalized Structured Component Analysis ◽  
The Relationship

Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze the structural relationships between observed variables and latent constructs. SEM has several methods one of which is Generalized Structured Component Analysis (GSCA). An empirical application concerning the relationship between renumeration and work motivation on employee performance is presented to illustrate the usefulness of the GSCA method. Data were collected by a questionnaire distributed to lecturers and staffs at UIN Sunan Kalijaga Yogyakarta. The result showed that the remuneration variable had a significant and positive impact on work motivation. Also, the work motivation variable had a significant and positive effect on employee performance.

scholarly journals PENERAPAN METODE GENERALIZED STRUCTURED COMPONENT ANALYSIS PADA KEPUASAN KONSUMEN (Studi Kasus: Pasien Klinik Q)

2020 ◽  
Vol 9 (4) ◽  
pp. 454-463
Author(s):  
Farisiyah Fitriani ◽  
Agus Rusgiyono ◽  
Tatik Widiharih
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Random Sampling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Component Analysis ◽  
Least Square ◽  
Equation Modeling ◽  
Generalized Structured Component Analysis ◽  
The Impact

Customer satisfaction is used by a company to evaluate products or services whether it is sufficient with customer’s expectations. Satisfaction is influenced by factors that cannot be measured directly are called latent variables and can be measured through indicators used to measure satisfaction with Structural Equation Modeling (SEM). Generalized Structured Component Analysis (GSCA) method is part of a SEM based on a variant that does not require the assumption of a multivariate normal distribution and has a measure overall goodness of fit. The parameters used are factor loading, coefficients parameter, and weight of indicators and estimated with alternating least square. The type of data used primary data from the results of the questionnaire with stratified proportional random sampling and number of samples 286. This research using indicators as measurable variables as many 32 indicators and 8 latent variable. Considering to the evaluation of the structural model, it is found there are 5 variables that influence satisfaction, they are prices, quality yield, cleanliness, doctor's services, and employee services with a large influence of 77.18% and the impact of satisfaction on loyalty is 48.63 %. For the overall goodness of fit measure, it is known that the FIT value is 63.75% and the adjusted FIT (AFIT) value is 63.47%. The goodness of fit (GFI) produced the value in the amount of 96.43%, indicating that the general model has the good level of compatibility.Keywords: Generalized Structured Component Analysis, Structural Equation Modeling, Overall goodness of fit, Alternating Least Square, Stratified Proportional Random Sampling

scholarly journals A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

2017 ◽  
Vol 65 (4) ◽  
pp. 359-365 ◽  
Author(s):  
Javier Senent-Aparicio ◽  
Jesús Soto ◽  
Julio Pérez-Sánchez ◽  
Jorge Garrido
Keyword(s):  
Frequency Analysis ◽  
Fuzzy Clustering ◽  
Optimal Number ◽  
Regional Frequency Analysis ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Homogeneous Regions ◽  
Optimal Number Of Clusters

AbstractOne of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.

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