ASSESSING THE EFFECTIVENESS OF CLUSTERING ALGORITHMS IN IDENTIFYING SALINITY DISTRIBUTIONS
Öz
In this study, the performances of various clustering algorithms were compared on salinity data to evaluate their effectiveness in classifying complex spatial patterns. The clustering methods applied included KMeans, Agglomerative Clustering, DBSCAN, MeanShift, Birch, MiniBatch KMeans, and Spectral Clustering. The silhouette score was used as the primary evaluation metric. According to the results, the MeanShift algorithm achieved the best performance with a silhouette score of 0.79, while KMeans and MiniBatch KMeans showed moderate success with scores of 0.38. Agglomerative Clustering, Birch, and DBSCAN yielded silhouette scores of 0.34, 0.31, and 0.28, respectively, whereas Spectral Clustering exhibited the poorest performance with a negative score of -0.35. These findings highlight that density-adaptive methods like MeanShift are particularly effective for analyzing heterogeneous and continuous oceanographic data. The sea water salinity dataset used in this study was obtained from the NOAA World Ocean Database (https://www.ncei.noaa.gov/products/world-ocean-database).
Anahtar Kelimeler
Clustering Analysis, Artificial Intelligence, Sea Water Salinity
Kaynakça
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