Abstract
The analysis of data, while interesting when a single variable is involved, becomes truly fascinating and challenging when several variables are present. There are various multivariate analysis methods available for examining the relationships among multiple variables simultaneously. Principal component analysis and cluster analysis are two commonly used techniques that are valuable tools in many scientific fields. Principal component analysis is employed to reduce the dimensionality of correlated measurements, whereas cluster analysis is utilized to classify objects or cases into relatively homogeneous groups. On the other hand, Ostracods can be utilized as bioindicators of the surrounding physical and chemical conditions. This paper presents a methodology for employing principal component analysis to cluster Ostracods based on their habitat preferences. Simulation results obtained using Mathematica software, demonstrate that anthropogenic water sources significantly influence the distribution of non-marine Ostracods.
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