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- W4386106130 abstract "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." @default.
- W4386106130 created "2023-08-24" @default.
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- W4386106130 date "2023-01-24" @default.
- W4386106130 modified "2023-09-25" @default.
- W4386106130 title "COMPARATIVE ANALYSIS OF K-MEANS AND FUZZY C-MEANS ALGORITHMS ON DEMOGRAPHIC DATA USING THE PCA METHOD" @default.
- W4386106130 doi "https://doi.org/10.25045/jpit.v14.i1.03" @default.
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