A Cluster Analysis of Identifying Team and Individual Sports Athlete Based on Anthropometric, Health and Skill Related Components

Kamarudin, Noor Aishah; Abdullah, Mohamad Razali; Eswaramoorthi, Vijayamurugan; Maliki, Ahmad Bisyri Husin Musawi; Rasid, Aina Munirah Ab; Majeed, Anwar P. P. Abdul; Razman, Mohd Azraai Mohd; Musa, Rabiu Muazu

doi:10.1007/978-981-19-2095-0_48

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…clusters are important in making decisions about the types of sports that would best suit athletes to achieve better results. 28 Identical and similar needs for the formation of clusters are generally encountered in the process of education and upbringing in the teaching of physical education, recreational and professional sports, etc. This work is primarily dedicated to improving the effectiveness of physical education teaching with the application of cluster methods.…”

Section: Paradigm Of Cluster Methods In Sports and Medicinementioning

confidence: 99%

Cluster methods in function better selection

Mijanović

2024

MOJSM

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Cluster analysis methods, also known as taxonomic methods, are intended for grouping objects and subjects according to certain characteristics, attributes and properties. Cluster analysis looks at relevant objects and attributes, classifying them into two or more independent groups. Cluster analysis supplemented with discriminant analysis is used in confirmatory and fundamental research. In numerous statistical-methodological procedures, these methods are applied when setting up and testing various hypotheses. Grouping methods are particularly useful in the process of different selections with the aim of forming coherent groups, which may or may not necessarily be statistically different. There are several models of clustering (grouping), always with one goal, which is greater proximity (similarity) of an entity belonging to a group compared to an entity belonging to another group. Two basic grouping models are recognizable, Hierarchical and Non-Hierarchical. Both models have the same goal, which is the formation of several independent homogeneous groups from one common group of entities. The hierarchical approach does not define the number of clusters in advance (a priori), in contrast to the Non-Hierarchical Model which defines in advance number of clusters. The grouping model is chosen depending on the specific problem and the set goal of grouping. In the process, several different models are often applied, and then one is chosen as in this research. It is important to point out that the theoretical number of clusters (groups) is often not realistically applicable in practice. Using the example of this research, it was proven that the first grouping was not a good solution. Through the subsequent, second and third iteration, as well as the application of additional discriminative methods, three optimal clusters were determined in the population of girls and boys. Satisfactory optimal grouping was obtained on the basis of gender criteria and achieved results on psycho-motor tests.

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Section: Paradigm Of Cluster Methods In Sports and Medicinementioning

confidence: 99%

Cluster methods in function better selection

Mijanović

2024

MOJSM

View full text Add to dashboard Cite

show abstract

Section: Paradigm Of Cluster Methods In Sports and Medicinementioning

confidence: 99%

Untitled

2024

MOJSM

View full text Add to dashboard Cite

Cluster analysis methods, also known as taxonomic methods, are intended for grouping objects and subjects according to certain characteristics, attributes and properties. Cluster analysis looks at relevant objects and attributes, classifying them into two or more independent groups. Cluster analysis supplemented with discriminant analysis is used in confirmatory and fundamental research. In numerous statistical-methodological procedures, these methods are applied when setting up and testing various hypotheses. Grouping methods are particularly useful in the process of different selections with the aim of forming coherent groups, which may or may not necessarily be statistically different. There are several models of clustering (grouping), always with one goal, which is greater proximity (similarity) of an entity belonging to a group compared to an entity belonging to another group. Two basic grouping models are recognizable, Hierarchical and Non-Hierarchical. Both models have the same goal, which is the formation of several independent homogeneous groups from one common group of entities. The hierarchical approach does not define the number of clusters in advance (a priori), in contrast to the Non-Hierarchical Model which defines in advance number of clusters. The grouping model is chosen depending on the specific problem and the set goal of grouping. In the process, several different models are often applied, and then one is chosen as in this research.It is important to point out that the theoretical number of clusters (groups) is often not realistically applicable in practice. Using the example of this research, it was proven that the first grouping was not a good solution. Through the subsequent, second and third iteration, as well as the application of additional discriminative methods, three optimal clusters were determined in the population of girls and boys. Satisfactory optimal grouping was obtained on the basis of gender criteria and achieved results on psycho-motor tests.

show abstract

A Cluster Analysis of Identifying Team and Individual Sports Athlete Based on Anthropometric, Health and Skill Related Components

Cited by 2 publications

References 14 publications

Cluster methods in function better selection

Cluster methods in function better selection

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