Enhancing CURE Algorithm with Stochastic Neighbor Embedding (CURE-SNE) for Improved Clustering and Outlier Detection - Ginting, Dewi Sartika Br; Efendi, Syahril; -, Amalia; Sihombing, Poltak (International journal of advanced computer science & applications, 2024)
This study focuses on analyzing stunting data using the CURE and CURE-SNE algorithms for clustering and outlier detection. The primary challenge is identifying patterns in stunting data, which includes variables such as age, gender, height, weight, and nutritional status. Both algorithms were employed to group the data and detect outliers that may affect the results of the analysis. The evaluation methods included determining the optimal number of clusters using the silhouette score and assessing cluster quality using the Davies-Bouldin Index (DBI). The results showed that both algorithms formed four clusters, with CURE-SNE detecting 6,050 outliers, while CURE detected 5,047 outliers. Silhouette score analysis revealed that both algorithms formed four optimal clusters. However, when validated using DBI, CURE achieved a score of 0.523, while CURE-SNE produced a lower score of 0.388, indicating that CURE-SNE outperformed CURE in terms of cluster quality. This suggests that CURE-SNE not only detects more outliers but also produces clusters with better separation and compactness. The findings highlight that both algorithms are effective for clustering stunting data, but CURE-SNE excels in terms of outlier detection and overall cluster quality. Thus, CURE-SNE is more suitable for handling complex datasets with potential outliers, providing more accurate insights into the structure of the data. In conclusion, CURE-SNE demonstrates superior performance compared to CURE, offering a more reliable and detailed clustering solution for stunting data analysis.