Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
Citation Count: Akyol, H.; Kızılduman, H.S.; Dökeroğlu, T. (2022). "Big Data Reduction and Visualization Using the K-Means Algorithm", Ankara Science University, Researcher, Vol.2, No.1., pp.40-45.
(2022) Akyol, Hakan; Kızılduman, Hale Sema; Dökeroğlu, Tansel; 234173
A huge amount of data is being produced every day in our era. In addition to high-performance processing
approaches, efficiently visualizing this quantity of data (up to Terabytes) remains a major difficulty. In this study,
we use the well-known clustering method K-means as a data reduction strategy that keeps the visual quality of the
provided huge data as high as possible. The centroids of the dataset are used to display the distribution properties
of data in a straightforward manner. Our data comes from a recent Kaggle big data set (Click Through Rate), and
it is displayed using Box plots on reduced datasets, compared to the original plots. It is discovered that K-means
is an effective strategy for reducing the amount of huge data in order to view the original data without sacrificing
its distribution information quality