ASSESSING THE EFFECTIVENESS OF CLUSTERING ALGORITHMS IN IDENTIFYING SALINITY DISTRIBUTIONS

Yıl 2025, Cilt: 1 Sayı: 39, 27 - 37, 30.07.2025

Perihan Karaköse

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

TUZLULUK DAĞILIMLARININ BELIRLENMESINDE KÜMELEME ALGORITMALARININ ETKINLIĞININ DEĞERLENDIRILMESI

Öz

Bu çalışmada, farklı kümeleme algoritmalarının tuzluluk verisi üzerindeki performansları karşılaştırılarak, karmaşık mekânsal desenleri sınıflandırmadaki başarıları incelenmiştir. Analizlerde KMeans, Agglomerative Clustering, DBSCAN, MeanShift, Birch, MiniBatch KMeans ve Spectral Clustering algoritmaları kullanılmıştır. Performans değerlendirmesi için siluet skoru temel ölçüt olarak kullanılmıştır. Elde edilen sonuçlara göre, MeanShift algoritması 0.79 siluet skoru ile en iyi sonucu verirken, KMeans ve MiniBatch KMeans algoritmaları 0.38 skoru ile orta düzeyde başarı göstermiştir. Agglomerative Clustering 0.34, Birch 0.31, DBSCAN 0.28 skoru elde etmiş, Spectral Clustering ise -0.35 skoruyla en düşük performansı sergilemiştir. Sonuçlar, özellikle heterojen ve sürekli değişim gösteren okyanus verilerinde yoğunluk adaptif yöntemlerin (örneğin MeanShift) üstün performans sunduğunu göstermektedir. Çalışmada kullanılan deniz suyu tuzluluk verisi, NOAA World Ocean Database’den temin edilmiştir (https://www.ncei.noaa.gov/products/world-ocean-database).

Anahtar Kelimeler

Kümeleme Analizi , Yapay Zeka , Deniz Suyu Tuzluluğu

Kaynakça

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