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A novel method for clustering basketball players with data mining and hierarchical algorithm
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S. Babaee Khobdeh   , M. R. Yamaghani , S. Khodaparast |
| Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran |
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Abstract: (892 Views) |
| The issue of talent identification in basketball is significantly underrepresented in the existing literature, despite its undeniable significance in the field of sports. The objective of this research is to use artificial intelligence algorithms in the talent detection procedure for basketball athletes. The timely identification of specific aptitudes in teenage athletes is a critical determinant of their prospective achievements. This research used player clustering techniques that included both individual and group talents in order to establish a talent detection approach for foundational age groups. The present investigation was carried out on a sample of 70 participants, ranging in age from 14 to 16 years. The algorithm described in this study demonstrated the improved performance in hierarchical clustering by using a combined strategy. This resulted in a greater level of accuracy when compared to comparable research endeavors. The primary objective of this approach is to provide a proficient aide for coaches and talent scouts. The findings of this study demonstrate a clustering accuracy of over 94 percent when categorizing players according to their talents and abilities, as compared to the evaluations provided by experienced coaches involved in this research endeavor. In summary, the present methodology shows a greater degree of precision in comparison to approaches used in analogous research investigations. |
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| Keywords: Clustering, Hierarchical Algorithm, Talent, Artificial Intelligence, Recognition |
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Full-Text [PDF 612 kb]
(575 Downloads)
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Type of Study: Research |
Subject:
General
Received: 2023/05/2 | Accepted: 2023/09/9
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