Segmentation of Partial Least Square Structural Equation Modeling Using Kernel K-Means Clustering (PLS SEM KKC) is the development of a new method in PLS SEM segmentation that uses non-linear clustering methods. Segmentation is carried out based on residual values of measurement and structural PLS SEM modeling which often exhibit non-linear separability and thus require a non-linear separable, thus requiring a non-linear PLS SEM segmentation. Main contribution of this study is the integration of kernel-based clustering into PLS SEM segmentation. Method effectively addresses unobserved heterogeneity by capturing non-linear residual patterns, leading to more accurate modelsSegmentation of Partial Least Square Structural Equation Modeling Using Kernel K-Means Clustering (PLS SEM KKC) is the development of a new method in PLS SEM segmentation that uses non-linear clustering methods. Segmentation is carried out based on residual values of measurement and structural PLS SEM modeling which often exhibit non-linear separability and thus require a non-linear separable, thus requiring a non-linear PLS SEM segmentation. Main contribution of this study is the integration of kernel-based clustering into PLS SEM segmentation. Method effectively addresses unobserved heterogeneity by capturing non-linear residual patterns, leading to more accurate models. This package is an extension of the ResiPLS package, which develops SEMPLS analysis with segmentation using Kernel K-Means. If you are interested in this package, please contact us at (cindy.cahyaning@umsida.ac.id).

Segmentation of Partial Least Square Structural Equation Modeling Using Kernel K-Means Clustering (PLS SEM KKC) is the development of a new method in PLS SEM segmentation that uses non-linear clustering methods. Segmentation is carried out based on residual values of measurement and structural PLS SEM modeling which often exhibit non-linear separability and thus require a non-linear separable, thus requiring a non-linear PLS SEM segmentation. Main contribution of this study is the integration of kernel-based clustering into PLS SEM segmentation. Method effectively addresses unobserved heterogeneity by capturing non-linear residual patterns, leading to more accurate modelsSegmentation of Partial Least Square Structural Equation Modeling Using Kernel K-Means Clustering (PLS SEM KKC) is the development of a new method in PLS SEM segmentation that uses non-linear clustering methods. Segmentation is carried out based on residual values of measurement and structural PLS SEM modeling which often exhibit non-linear separability and thus require a non-linear separable, thus requiring a non-linear PLS SEM segmentation. Main contribution of this study is the integration of kernel-based clustering into PLS SEM segmentation. Method effectively addresses unobserved heterogeneity by capturing non-linear residual patterns, leading to more accurate models. This package is an extension of the ResiPLS package, which develops SEMPLS analysis with segmentation using Kernel K-Means. If you are interested in this package, please contact me at (cindy.cahyaning@umsida.ac.id).

Segmentation of Partial Least Square Structural Equation Modeling Using Kernel K-Means Clustering (PLS SEM KKC) is the development of a new method in PLS SEM segmentation that uses non-linear clustering methods.

This package is an extension of the ResiPLS package, which develops SEMPLS analysis with segmentation using Kernel K-Means.