K-means Clustering in R Libraries {cluster} and {factoextra} for Grouping Oceanographic Data

Abstract

Cluster analysis by k-means algorithm by R programming is the scope of the current paper. The study assesses the similarity of the sampling data derived from the GIS project by homogeneity of their attribute parameters aimed to analyze similar clusters of the observa- tion data by the variety of parameters: geology (similar location on the tectonic plates, sediment thickness, igneous volcanic areas), bathymetry (similar depth ranges) and geomorphology (similar slope steepness and aspect). The geological case study is Mariana Trench. Clustering as ef- fective statistical method to detect similar groups in the data set. Tech- nically, major used R libraries include {cluster}, {factoextra}, {ggplot2}. Minor R libraries include {wordcloud}, {tm}. Several clusters were tested from 2 to 7, optical number is 5. The findings include following computed and visualized results illustrated by 8 figures: 1) correlation matrix show- ing crossing correlations in the combination of factors; 2) comparison of the bi-factors in-between the factors revealed pairwise correlation; 3) pairwise comparative analysis enabled to observe an influence on the variables as bi-factors: in response to the decreasing sediment thickness, slope angles go in parallel; 4) the location of the volcanic igneous ar- eas cause a cyclic repetition of the curve for the slope angles, and those of the volcanic zones have correlation with the slope angle and aspect degree. Findings reveals that four variables affect geomorphology of the trench: slope angle, sediment thickness, aspect degree and volcanic ig- neous areas. The paper includes 7 listings of R programming codes for repeatability of the algorithms in similar research.

Keywords

• R,programming language,statistics,geospatial data,k-means clustering,cluster analysis,data grouping,marine geology

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