Finding playing styles of badminton players using firefly algorithm-based clustering algorithms

• Autorzy: Ilankoon I.M.T.P.K. , Samarasinghe U.S. , Ariyaratne M.K.A. , Silva R. M.

Identyfikatory

DOI

10.7494/csci.2023.24.3.5116

• Języki publikacji: EN

Abstrakty

EN

Cluster analysis can be defined as applying clustering algorithms with the goal of finding any hidden patterns or groupings in a data set. Different clustering methods may provide different solutions for the same data set. Traditional clustering algorithms are popular, but handling big data sets is beyond the abilities of such methods. We propose three big data clustering methods basedon the firefly algorithm (FA). Three different fitness functions were definedon FA using inter-cluster distance, intra-cluster distance, silhouette value, and the Calinski-Harabasz index. The algorithms find the most appropriate cluster centers for a given data set. The algorithms were tested with nine popular synthetic data sets and one medical data set and are later applied on two badminton data sets with the intention of identifying the different playing styles of players based on their physical characteristics. The results specify that the firefly algorithm could generate better clustering results with high accuracy. The algorithms cluster the players to find the most suitable playing strategy for a given player where expert knowledge is needed in labeling the clusters. Comparisons with a PSO-based clustering algorithm (APSO) and traditional algorithms point out that the proposed firefly variants work in a similar fashion as the APSO method, and they surpass the performance of traditional algorithms.

Słowa kluczowe

EN

firefly algorithm; clustering; intra- and inter-cluster distance; badminton; intera-cluster distance; inter-cluster distance

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2023
• Tom: T. 24 (3)
• Strony: 427--450
• Opis fizyczny: Bibliogr. 39 poz., rys., tab.

Twórcy

autor

Ilankoon I.M.T.P.K.

• University of Sri Jayewardenepura, Department of Computer Science, Faculty of AppliedSciences, Nugegoda, Sri Lanka

autor

Samarasinghe U.S.

• University of Sri Jayewardenepura, Department of Computer Science, Faculty of AppliedSciences, Nugegoda, Sri Lanka

autor

Ariyaratne M.K.A.

• University of Sri Jayewardenepura, Department of Computer Science, Faculty of AppliedSciences, Nugegoda, Sri Lanka

autor

Silva R. M.

• University of Sri Jayewardenepura, Department of Statistics, Faculty of Applied Sciences, Nugegoda, Sri Lanka

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