Efficacy of Clustering Algorithms for Young ‘Nam-Hom’ Coconut Gene Expression Data in Unveiling the Specific Genes Determining the Flavor: A Comparative Analysis of K-means and Fuzzy C-means

This study explores the application of K-means and Fuzzy C-means clustering techniques to analyze gene expression data related to the flavor of young ‘Nam-Hom’ coconuts. By comparing these clustering methods, the research aims to identify gene clusters that significantly influence the aromatic and off-flavor profiles of young ‘Nam-Hom’ coconuts stored at different temperatures (4°C and 25°C). Specifically, our findings highlight clusters involved in lipid metabolism and cold stress response which are crucial for developing desirable and undesirable flavors, such as LOX1 and ADH2 genes. The study advances our understanding of coconut genetics demonstrates the utility of clustering techniques in agricultural genomics, offering valuable pathways for future genetic enhancement and storage optimization strategies aimed at improving coconut aroma.