An algorithm to discover spatial–temporal distributions of physical seawater characteristics and a case study in Turkish seas

Clustering is one of the major data mining methods to obtain a number of clues about how the physical properties of the water are distributed in a marine environment. It is a difficult problem, especially when we consider the task for spatial–temporal marine data. This study introduces a new clustering algorithm to discover regions that have similar physical seawater characteristics. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to the nonspatial, spatial, and temporal values of the objects. Our algorithm also overcomes three drawbacks of existing clustering algorithms: problems in the identification of core objects, noise objects, and adjacent clusters. This paper also presents a spatial–temporal marine data warehouse system designed for storing and clustering physical data from Turkish seas. Special functions were developed for data integration, data conversion, querying, visualization, analysis, and management. User-friendly interfaces were also developed, allowing relatively inexperienced users to operate the system. As a case study, we show the spatial–temporal distributions of sea surface temperature, sea surface height residual, and significant wave height values in Turkish seas to demonstrate our algorithm.