Use of the K-Medoids Algorithm for Food Clustering Using Nutritional Value and Evaluation of the Elbow Method and the Davies Bouldin Index Method

Abstract

The six categories of necessary nutrients water, minerals, vitamins, carbs, proteins, and fats must be present in the food that people eat on a daily basis. Humans require nutrition since it will enable them to do everyday duties and maintain their health. The pupose of this study is to classify foods with comparable nutritional values. Foods with high, medium, and low nutritional levels are grouped into three clusters. This study applies the K-Medoids algorithm optimization to the clustering approach. The study’s clustering results can be utilized to choose and consume foods that will meet nutritional needs and help delay the onset of food related disorders. For instance, if you wish to gain weight, you can choose foods in cluster 0. Cluster 2 foods can be picked if you wish to diet or lose weight, while Cluster 1 meals can serve as a benchmark if taken in excess, as this can lead to obesity.