EJONS INTERNATIONAL JOURNAL ON MATHEMATICS, ENGINEERING & NATURAL SCIENCES ISSN 2602 - 4136

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Abstract



Data mining is a wide field for researchers because of their various approaches to information discovery in large volumes of data stored in different formats. By applying data mining techniques, the discovery of unknown patterns and the relationships between the data are discovered. It is classified as classification, clustering, feature selection and association rule mining based on different functions of data mining. The rules of association are aimed at identifying the characteristics of interrelated data and determining the magnitude of the relationships between them. The most commonly used association rule algorithms are Apriori and FP-Growth algorithms. The aim of this study is to show the usage of these rule extraction methods used in basket market analysis in different studies. For this purpose, the study of social media usage trends of high school students was modeled with Fp-Growth algorithm. As a result of the model, it is seen that although students have privacy problems, they do not refrain from sharing in social media tools. It was observed that family and community pressures had a significant effect on students.



Keywords
Association rules, Data mining, Fp-Growth Algorithm



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