Data Mining in Elite Beach Volleyball – Detecting Tactical Patterns Using Market Basket Analysis

Wenninger, Sebastian; Link, Daniel; Lames, Martin

doi:10.2478/ijcss-2019-0010

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…As far as the breadth of mining means and the depth of the mining content are concerned, it is far from enough, so these deficiencies will be the direction and task of the follow-up study. e Apriori technique is used for the recurrent element set mining [14]. It is also based on the learning of the association rule over relational Database Management systems (DBMS).…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Volleyball Competition Using Machine Learning and Edge Intelligence

Liu

2021

Mobile Information Systems

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Data analysis and machine learning are the backbones of the current era. Human society has entered machine learning and data science that increases the data capacity. It has been widely acknowledged that not only does the number of information increase exponentially, but also the way of human information management and processing is completed to be changed from manual to computer, mainly depending on the transformation of information technology including a computer, network, and communication. This paper is aimed at a solution to the lag of the methods and means of volleyball technique prediction in China. Through field visits, it is found that the way of analysis and research of techniques and tactics in Chinese volleyball practice is relatively backward, which to a certain extent affected the rapid development of Chinese volleyball. Therefore, it is a necessary and urgent task to realize the reform of the methods and means of volleyball technical and tactical analysis in China. The data analysis and prediction are based on the machine learning and data mining algorithm applied to volleyball in this paper is an inevitable trend. The proposed model is applied to the data produced at the edges of the systems and thoroughly analyzed. The Apriori algorithm of the machine learning algorithm is utilized to process the data and provide a prediction about the strategies of a volleyball match. The Apriori algorithm of machine learning is also optimized to perform better data analysis. The effectiveness of the proposed model is also highlighted.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Volleyball Competition Using Machine Learning and Edge Intelligence

Liu

2021

Mobile Information Systems

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Specific polysubstance use patterns predict relapse among patients entering opioid use disorder treatment

Pan¹,

Feaster²,

Odom³

et al. 2022

Drug and Alcohol Dependence Reports

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An User Intention Mining Model Based on Fractal Time Series Pattern

2020

Fractals

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Users use the network more and more frequently, and more and more data is published on the network. Therefore, how to find, organize, and use the useful information behind these massive data through effective means, and analyze user intentions is a huge challenge. There are many time series problems in user intentions. Time series have complex characteristics such as randomness and multi-scale variability. Effectively identifying the inherent laws and objective phenomena contained in time series is the purpose of analyzing and processing time series data. Fractal theory provides a new way to analyze time series, and obtains the characteristics and rules of time series from a new perspective. Therefore, this paper introduces the fractal theory to analyze the time series problem, and proposes an improved G-P algorithm to realize the prediction and mining of user intentions. First, the method of array storage instead of repeated calculations is used to improve the method of saturated correlation dimension. Second, the Hurst exponent of the time series is obtained by the variable scale range analysis method. Finally, a fractal model for predicting user intent in short time series is established using the accumulation and transformation method. The experimental results show that the use of fractal theory can effectively describe the relevant characteristics of time series, the development trend of user intentions can be mined from big data, and the prediction model for short time series can be established to achieve information mining of user intentions.

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Data Mining in Elite Beach Volleyball – Detecting Tactical Patterns Using Market Basket Analysis

Cited by 4 publications

References 32 publications

Prediction of Volleyball Competition Using Machine Learning and Edge Intelligence

Prediction of Volleyball Competition Using Machine Learning and Edge Intelligence

Specific polysubstance use patterns predict relapse among patients entering opioid use disorder treatment

An User Intention Mining Model Based on Fractal Time Series Pattern

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