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DOI: 10.14569/IJACSA.2023.01403102
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Method for Inferring the Optimal Number of Clusters with Subsequent Automatic Data Labeling based on Standard Deviation

Author 1: Aline Montenegro Leal Silva
Author 2: Francisco Alysson da Silva Sousa
Author 3: Alysson Ramires de Freitas Santos
Author 4: Vinicius Ponte Machado
Author 5: Andre Macedo Santana

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.

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Abstract: Machine learning is a suitable pattern recognition technique for detecting correlations between data. In the case of unsupervised learning, the groups formed from these correlations can receive a label, which consists of describing them in terms of their most relevant attributes and their respective ranges of values so that they are understood automatically. In this research work, this process is called labeling. However, a challenge for researchers is establishing the optimal number of clusters that best represent the underlying structure of the data subjected to clustering. This optimal number may vary depending on the data set and the grouping method used and influences the data clustering process and, consequently, the interpretability of the generated groups. Therefore, this research aims to provide an inference approach to the number of clusters to be used in the grouping based on the range of attribute values, followed by automatic data labeling based on the standard deviation to maximize the understanding of the groups obtained. This methodology was applied to four databases. The results show that it contributes to the interpretation of the groups since it generates more accurate labels without any overlap between ranges of values, considering the same attribute in different groups.

Keywords: Inference approach; range of attribute values; labeling; standard deviation; interpretation of the groups

Aline Montenegro Leal Silva, Francisco Alysson da Silva Sousa, Alysson Ramires de Freitas Santos, Vinicius Ponte Machado and Andre Macedo Santana, “Method for Inferring the Optimal Number of Clusters with Subsequent Automatic Data Labeling based on Standard Deviation” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01403102

@article{Silva2023,
title = {Method for Inferring the Optimal Number of Clusters with Subsequent Automatic Data Labeling based on Standard Deviation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01403102},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01403102},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Aline Montenegro Leal Silva and Francisco Alysson da Silva Sousa and Alysson Ramires de Freitas Santos and Vinicius Ponte Machado and Andre Macedo Santana}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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