Mathematics, Volume 11, Issue 3
2023 February-1 - 297 articles
Cover Story: The most popular group of unsupervised machine learning methods is clustering methods. The main goal of clustering is to find hidden relationships between observations. Sometimes, to use clustering methods, the initial data are so large that it becomes difficult. To implement this, one of the ways is to use data dimensionality reduction and include them in the clustering method. This paper presents the extension to the clustering method based on the modified inversion formula density estimation to solve previous method limitations. This new method’s extension works within higher-dimension (d > 15) cases, which was the limitation of the previous method. The new modification method has better results than the standard method in all cases, which confirmed the hypothesis about the new method’s positive impact on clustering results. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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