Examination of principal component analysis on hierarchical clustering methods in terms of dimension reduction

In the light of technological and scientific developments, with the introduction of concepts such as artificial intelligence and machine learning into our lives, data production in the world is increasing exponentially. Parallel to this, data has become a precious mine that can be bought, processed, converted into meaningful and important information, and sold. This rapid and huge increase causes many difficulties in the process of transforming data into information. One of these difficulties is that the data to be analyzed is too large or too dimensional. These features of the data make it difficult for the applied analysis technique to work correctly and comfortably. To cope with these problems, there are many methods applied in the pre-processing stages of data. This thesis study purposes examine the effect of the Principal Component Analysis method on Hierarchical Clustering techniques in terms of dimensionality reduction in high-dimensional data sets. The study was carried out using Principal Component Analysis and Hierarchical Clustering methods on the data sets with 22, 38, and 46 variables, created with 2020 data from the United Nations data platform. The results obtained from the analysis were compared and interpreted using tanglegrams and some similarity coefficients. The results of the study showed that the Principal Component Analysis method had positive effects on hierarchical clustering results and dendrograms despite low correlation and outliers.