K-means cluster analysis of the West African species of cereals based on nutritional value composition
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CitationAtsaam, Donald Douglas. Oyelere, Solomon. Balogun, Oluwafemi Samson. Wario, Ruth. Blamah, NV. (2021). K-means cluster analysis of the West African species of cereals based on nutritional value composition. African journal of food, agriculture, nutrition and development, 21 (1) , 17195-17212. 10.18697/ajfand.96.19775.
TheK-means algorithm was deployed to extract clusters within the prevalent cereal foodsin West Africa. The West AfricaFood Composition Table (WAFCT) presents all the 76 food sources in the cereals class as a single group without considering thesimilarity or dissimilarityin nutritional values. Using K-means clustering, the Euclidean distance betweennutritional values of all cereal food items were measured to generate six sub-groupsbased on similarity. A one-way analysis to validate the results of the extracted clusters was carried out using the mean squarevalues. For everynutrient, the “within groups” and “between groups” valuesof the mean squares were examined. This was done to ascertain how similar or dissimilar data points in the same or different clusters were to each other. It was discovered that the P valuesfor all “between groups” and “within groups” mean squaresfor every nutrient was P < 0.01. Additionally, it was observed that in all cases, the mean square valuesof the “within groups” weresignificantly lower than those of the “between groups”. These outcomes are indications that clustering was properly done such that the variability in nutrient values for all food sources within the same clusters wassignificantly low,while those in different clusters were significantly high. Thus, the ultimate objectiveof clustering,which is to maximize intra-cluster similarity and minimize inter-cluster similaritywas effectively achieved. Cluster analysis in thisstudy showed that all food items within a particular cluster are similar to each other and dissimilar to food items in a different cluster.These findings are valuable in dietaries, food labeling, raw materials selection, public health nutrition, and foodscience research,whenansweringquestionsonthe choice ofalternative food items. Whereoriginal choices are not available or unaffordable, the clusters can be explored to select other similar options within the same cluster as the original choice.