Cluster analysis: application of K-means algorithm to explore the sea surface temperature and salinity patterns

Keyan Ding; Zihan Liu; Mo Wang

doi:10.1117/12.3025844

8 April 2024 Cluster analysis: application of K-means algorithm to explore the sea surface temperature and salinity patterns

Keyan Ding, Zihan Liu, Mo Wang

Author Affiliations +

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130902J (2024) https://doi.org/10.1117/12.3025844
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

This paper investigates how the sea surface temperature (SST) and salinity (SSS) change with the seasons based on K-means clustering analysis. For index k, the elbow method is used to estimate the value. Then this paper analyzes the clustering of SSS and SST in a specific region that contains both characteristic SST and SSS features and compare the clustering figures from each season. Meanwhile, by comparing K-means cluster results computed from raw data to those obtained for classified histograms, we find that it is ideal to combine these two types of data to do an analysis. To illustrate our methodology, we use the Silhouette Coefficient to find the goodness of our K-means cluster, and the calculated Silhouette Coefficient equals 0.8, which illustrates our model has relatively high quality and accuracy. Lastly, we apply other clusters, such as DBSCAN and Gaussian Mixture Model to the same data, but the results indicate the difference between our data is too weak to be clustered by these two methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Keyan Ding, Zihan Liu, and Mo Wang "Cluster analysis: application of K-means algorithm to explore the sea surface temperature and salinity patterns", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130902J (8 April 2024); https://doi.org/10.1117/12.3025844

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