Analysis of Preference Clustering and Strategies for Utilizing Sources and Media in Accessing Academic References by College Students Using the K-Means and DBSCAN Algorithms
Keywords:
Principal Component Analysis, K-Means, DBSCAN, Clustering, Academic ReferencesAbstract
The increasing availability of academic information sources has led to diverse preferences and strategies among university students in searching for and utilizing academic references. This study aims to identify patterns in students’ academic reference-seeking behavior by applying Principal Component Analysis (PCA) and comparing the clustering performance of K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Data were collected through an online questionnaire involving 150 university students, with ten numerical variables used in the analysis. The data were preprocessed, standardized using StandardScaler, and reduced using PCA before clustering. The results showed that PCA reduced the ten variables to six principal components while retaining 83.53% of the cumulative variance. K-Means produced two clusters containing 123 and 27 respondents, achieving a Silhouette Score of 0.4543, a Calinski-Harabasz Index (CHI) of 52.2985, and a Davies-Bouldin Index (DBI) of 1.4225. Meanwhile, DBSCAN, with eps = 2.3 and min_samples = 2, identified two clusters consisting of 137 and 2 respondents, along with 11 noise points. DBSCAN achieved a Silhouette Score of 0.5469, a CHI of 15.3191, and a DBI of 0.4818. Therefore, DBSCAN was selected for further analysis to identify differences in students’ academic reference-seeking behaviors, particularly in source verification, reference usage, and manual and AI-assisted searching.
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