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Updated: Jan 18, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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Published on: February 15, 2017

Segmentation of partial least squares structural equation modelling using kernel K-means clustering (PLS SEM KKC).

Cindy Cahyaning Astuti^1,2, Bambang Widjanarko Otok¹, Shofi Andari¹

¹Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.

|September 8, 2025

Summary

This study introduces PLS SEM Kernel K-Means Clustering (PLS SEM KKC) for improved segmentation. This novel method effectively addresses unobserved heterogeneity by capturing non-linear patterns, significantly enhancing Partial Least Squares Structural Equation Modeling accuracy.

Keywords:

Kernel K-Means Clustering PLS SEM Segmentation

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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Published on: August 13, 2014

Area of Science:

Statistical Modeling
Machine Learning Applications
Multivariate Data Analysis

Background:

Partial Least Squares Structural Equation Modeling (PLS SEM) is widely used but limited by unobserved heterogeneity.
Existing PLS SEM segmentation methods rely on linear clustering, failing to capture non-linear residual patterns.
Unobserved heterogeneity can lead to inaccurate and unreliable PLS SEM models.

Purpose of the Study:

To propose and evaluate a novel non-linear segmentation method for PLS SEM, named PLS SEM Kernel K-Means Clustering (PLS SEM KKC).
To address the limitation of unobserved heterogeneity in PLS SEM by incorporating kernel-based clustering.
To enhance the accuracy and reliability of PLS SEM models through effective segmentation.

Main Methods:

Developed PLS SEM Kernel K-Means Clustering (PLS SEM KKC) by integrating kernel-based clustering with PLS SEM.
Segmentation was performed based on the non-linear residual values from measurement and structural models of a global PLS SEM.
Employed clustering to group observations with similar residual patterns into homogeneous segments.

Main Results:

The PLS SEM KKC method significantly improved model accuracy compared to the global model.
R² values increased from 51.1% (global model) to 93.9% (k=2) and 97.5% (k=3) in segmented clusters.
The substantial increase in local R² demonstrates the successful overcoming of unobserved heterogeneity.

Conclusions:

PLS SEM KKC is a recommended new method for PLS SEM segmentation.
The method effectively captures non-linear residual patterns, successfully addressing unobserved heterogeneity.
PLS SEM KKC leads to more accurate and robust PLS SEM models by creating homogeneous segments.