COMPARATIVE ANALYSIS OF K-MEANS AND FUZZY C-MEANS ALGORITHMS ON DEMOGRAPHIC DATA USING THE PCA METHOD

The concept of demography, which includes the processes such as birth, death, natural increase, improvement of employment and standard of living of the population, migration, etc., occupies a unique place in the global processes of the modern era. In this regard, this article uses clustering algorithms, which are estimated as a demographic data mining technology. For the analysis of demographic data, experiments are performed using k-means and fuzzy c-means clustering algorithms in the Python programming language. The experiment uses PCA method to reduce the dimension and get more effective results. Silhouette, Calinski-Harabasz and Davies-Bouldin indices, and CPU time are used to evaluate the quality of the algorithm. The result of the experiment shows the possibility of achieving an effective result through the k-means and fuzzy c-means clustering algorithms by applying the PCA method in the demographic data analysis (pp.15-22).