Model of the text classification system using fuzzy sets

Salahor, Dmytro; Smółka, Jakub

doi:10.35784/jcsi.2634

Cited by 2 publications

(1 citation statement)

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“…Fuzzy modelling as well as data analysis methods, including clustering, have become very important aspects of many studies. Clustering is a useful approach, which allows division of a dataset into groups with the most similar features while the dissimilar data points are separated Benjamin and Yang (2021); Simpson (1993); Salahor and Smo lka (2021). As a result data are divided into different clusters.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C-Means Clustering for Tennis Multivariate Time-Series

Skublewska‐Paszkowska

Karczmarek

Powroźnik

et al. 2023

Preprint

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Creating the proper player profile in training is crucial for athlete development. Although there is a great number of studies concerning this subject, there is no solution that would allow to model it in a convenient way. Applying fuzzy modelling clustering can be useful in this field. Moreover, the application of sophisticated acquisition techniques, like motion capture systems, allow ones to obtain accurate data corresponding to athlete’s movement in the form of a multivariate time series. In this study, the authors undertook the task of clustering the most important at the stage of training tennis strokes such as: Forehand, backhand, and volley. They were represented as trajectories of the tennis racket based on four retro-reflective markers attached to it. The Fuzzy C-Means algorithm, which utilizes the dynamic time warping-based distance to cluster analysis of tennis strokes, has been applied with success to group various kinds of movement of tennis players. The comprehensive analysis included numerous separate tennis moves and their groups. Various analyses depending on their number have been thoroughly carried out. The obtained results allowed creation of the reference stroke model,which can be used for further examination of the tennis players’performance.

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Section: Introductionmentioning

confidence: 99%