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Cluster analysis is a useful data mining method to obtain detailed information on the physical state of the ocean. The primary objective of this study is the development of a new spatio-temporal density-based algorithm for clustering physical oceanographic data. This study extends the regular spatial cluster analysis to deal with spatial data at different epochs. It also presents the sensitivity of the new algorithm to different parameter settings. The purpose of the sensitivity analysis presented in this paper is to identify the response of the algorithm to variations in input parameter values and boundary conditions. In order to demonstrate the usage of the new algorithm, this paper presents two oceanographic applications that cluster the sea-surface temperature (SST) and the sea-surface height residual (SSH) data which records the satellite observations of the Turkish Seas. It also evaluates and justifies the clustering results by using a cluster validation technique.
The Journal of Marine Research publishes peer-reviewed research articles covering a broad array of topics in physical, biological and chemical oceanography. Articles that deal with processes, as well as those that report significant observations, are welcome. In the area of biology, studies involving coupling between ecological and physical processes are preferred over those that report systematics. Authors benefit from thorough reviews of their manuscripts, where an attempt is made to maximize clarity. The time between submission and publication is kept to a minimum; there is no page charge.