Modeling and analyzing marine data using data mining techniques

The research presented in this thesis is an interdisciplinary work that combines computer science and marine science. It provides new computer based approaches, techniques and technologies for (i) modeling, collecting, archiving marine data, (ii) analyzing and mining marine data by using data mining techniques and (iii) visualizing marine data. It presents my efforts on the collecting physical, biological, chemical marine data, some explanations about the visualization of marine data on the map, my works on the construction of decision trees to classify physical marine data. This thesis introduces two new data mining algorithms: one is for clustering spatio-temporal data and the other is for spatio-temporal outlier detection in data warehouses. It also proposes a new approach: web service-based parallel clustering which includes the parallel execution of web services for discovering clusters in large data warehouses. In addition to new clustering algorithm, this thesis also presents the validation and evaluation of the clustering results of this clustering algorithm. It shows the mathematical quality and reliability of the clustering results by using a cluster validation technique. It also presents the sensitivity analysis of the new clustering algorithm to the input parameters.