Data Mining: A Preprocessing Engine
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Copyright: © 2021 Luai A. Shalabi, Zyad Shaaban and Basel Kasasbeh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A new matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded.
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