Ocean data mining with application to climate indices of the North Atlantic
This study is a part of the research project on development of a database and methods for data mining of ocean data. The first part of the project describes the implementation of the relational database management system (RDBMS) for ocean data. The second part of the project introduces a clustering...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
Memorial University of Newfoundland
2010
|
| Subjects: | |
| Online Access: | https://research.library.mun.ca/10594/ https://research.library.mun.ca/10594/1/Hakobyan_Madlena.pdf |
| Summary: | This study is a part of the research project on development of a database and methods for data mining of ocean data. The first part of the project describes the implementation of the relational database management system (RDBMS) for ocean data. The second part of the project introduces a clustering method for identification of regions with homogeneous behavior of ocean parameters. Three algorithms K-means, Expectation Maximization (EM), and Farthest-First (FF) were implemented and evaluated in applications to the sea surface temperature data (SST). The clustering method was applied in analysis of two climate indices of the North Atlantic Ocean derived from the past observations of SST. The first one is associated with the North Atlantic Oscillation (NAO) and the second one with the variability of the Meridional Overturning Circulation (MOC). The two climate indices capture the most important long term variability of MOC and NAO. |
|---|