An Improved Hybrid CURE–SNE Model for High-Dimensional Data Clustering

Abstract: Stunting remains a critical public health issue in rural communities, largely driven by inadequate nutrition, poor sanitation, and unfavorable socioeconomic conditions. This study proposes a hybrid clustering approach by integrating Clustering Using Representatives (CURE) with t-distributed Stochastic Neighbor Embedding (t-SNE) to analyze stunting prevalence and support the optimization of child nutrition strategies. Secondary data were collected from publicly accessible national health and nutrition repositories, comprising 500 child records with multiple parameters, including anthropometric indicators, nutritional intake, maternal characteristics, environmental sanitation, and socioeconomic factors. The t-SNE algorithm was employed to reduce the high-dimensional data into a two-dimensional space while preserving neighborhood structures, followed by the application of the CURE algorithm to construct clusters that are robust to noise and outliers. Experimental results indicate that the proposed CURE–SNE approach successfully formed four distinct clusters, namely C1 Very High Stunting Risk with 128 data points (25.6%), C2 High Stunting Risk with 142 data points (28.4%), C3 Moderate/Transitional Stunting Risk with 117 data points (23.4%), and C4 Low Stunting Risk with 113 data points (22.6%). Cluster quality evaluation demonstrates that the hybrid CURE–SNE method achieves a higher Silhouette Score and a lower Davies Bouldin Index compared to the CURE only approach, indicating improved cluster separation and compactness. These findings confirm that combining dimensionality reduction with representative-based clustering enhances the interpretability of stunting patterns and provides a reliable analytical foundation for designing targeted and data-driven child nutrition interventions in rural settings.

Dewi Sartika Br Ginting, T. H. F. Harumy, Ade Sarah Huzaifah and Ivanny Putri Marianto. “An Improved Hybrid CURE–SNE Model for High-Dimensional Data Clustering”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170339

@article{Ginting2026,
title = {An Improved Hybrid CURE–SNE Model for High-Dimensional Data Clustering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170339},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170339},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Dewi Sartika Br Ginting and T. H. F. Harumy and Ade Sarah Huzaifah and Ivanny Putri Marianto}
}