Ji Hoon Ryoo1,*, Seohee Park2, Seongeun Kim3, Heungsun Hwang4
CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 801-822, 2022, DOI:10.32604/cmes.2022.019708
- 27 June 2022
Abstract 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… More >
Login
Register
