The implementation of K-Means clustering in kovats retention index on gas chromatography

Abstract

In this study, the retention index data of 146 compounds that are found in coal and petroleum-derived liquid fuels were grouped using the K-means clustering method, and the similarities between each cluster were analyzed. The psycho-chemical properties of each compound in the cluster were identified and compared with other clusters. Each compound’s retention index is grouped based on the similarity between the column polarity and heating rate of one compound to another. Based on the results of tests carried out on nine differentk values, it is known that the grouping with the value of k = 3 is the best determined from the obtained silhouette score = 0.568, where this score is higher than the score obtained on the other k values. The results of clustering with k = 3 obtained three clusters, namely cluster C1, cluster C2, and cluster C3. Cluster C1 and cluster C2 consist of chemical compounds that have a relatively low carbon number and molecular mass, but in cluster C2 the molecular mass of the compound is lower than in cluster C1. In contrast, the C3 cluster consists of chemical compounds that have a relatively high carbon number and molecular mass.

Real-Time Monitoring the Effects of Storage Conditions on Volatile Compounds and Quality Indexes of Halal-Certified Kimchi during Distribution Using Electronic Nose

“…Cluster analysis was used to normalize the concentration of volatile compounds by squared Euclidean and applied the standard k-means to a dataset of identified VOCs in the Kimchi. Data mining and clustering related to chemometric databases are common in chemical processing data [ 52 , 53 ]. In the same way, hierarchical cluster analysis can be used to find the correlation between volatile compounds and sensory attributes of traditional sweets [ 54 ].…”

Statistical Assessment of Human Development Index Variations and Their Correlates: A Case Study of Aceh Province, Indonesia

“…It helps us understand whether there is a positive correlation (when one variable goes up, the other also goes up), a negative correlation (when one variable goes up, the other goes down), or no correlation at all. Correlation tests provide important insights in data analysis, allowing us to identify relationships that may exist between the variables, which in turn can help in decision-making, planning, or further research [40,41].…”

Environmental and Economic Clustering of Indonesian Provinces: Insights from K-Means Analysis

“…The Kmeans algorithm iteratively assigns each data point to the nearest centroid and then recalculates the centroids based on the mean of all data points assigned to each cluster. The objective function of K-means can be defined as shown in Equation 1 [22,30].…”