Hierarchical Cluster Analysis by R language for Pattern Recognition in the Bathymetric Data Frame: A Case Study of the Mariana Trench, Pacific Ocean

Virtual Simulation, Prototyping and Industrial Design. Proceedings of 5th International Scientific-Practical Conference. Vol. 2, Issue 5. pp. 147–152. Ed. M. N. Krasnyansky. Tambov State Technical University, Russia. ISBN: 978-5-8265-1997-4, 2018

6 Pages Posted: 10 Jan 2019

Date Written: November 14, 2018

Abstract

The geographic focus of the current study Mariana trench, the deepest point of the Earth located in the west Pacific Ocean. Mariana trench has unique structure and features formed in the complex process of the trench development. There is a range of the environmental factors affecting trench structure and functioning: bathymetry, geography, geology and tectonics. Current research aimed to study interconnections among these determinants. Technically, the research was performed by R programming language, statistical analysis, and QuantumGIS. Methodology includes a range of the statistical methods for data processing, the most important of which is cluster analysis. The results revealed unevenness of the factors affecting trench bathymetric structure, caused by the environmental conditions.

Keywords: Cluster analysis, R, programming, algorithms, oceanography, Mariana Trench, Pacific Ocean

JEL Classification: Y92, Y10, Q01, Q25, Q55, Q59

Suggested Citation

Lemenkova, Polina, Hierarchical Cluster Analysis by R language for Pattern Recognition in the Bathymetric Data Frame: A Case Study of the Mariana Trench, Pacific Ocean (November 14, 2018). Virtual Simulation, Prototyping and Industrial Design. Proceedings of 5th International Scientific-Practical Conference. Vol. 2, Issue 5. pp. 147–152. Ed. M. N. Krasnyansky. Tambov State Technical University, Russia. ISBN: 978-5-8265-1997-4, 2018. Available at SSRN: https://ssrn.com/abstract=3307935

Polina Lemenkova (Contact Author)

Ocean University of China ( email )

5 Yushan Road
Qingdao, 266003
China

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