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A Study on the Application of Fuzzy Clustering Analysis in Juvenile Delinquency Prevention

机译:模糊聚类分析在青少年犯罪预防中的应用研究

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In recent years, the application of data mining technology in juvenile delinquency prevention has been receiving growing attention. The clustering algorithm of machine learning can divide data into many clusters according to grouping targets and its main purpose is to find several similar clusters among the data. The fuzzy clustering analysis method has excellent performance in clustering distribution rate. This study applies the Fuzzy C-means (FCM) analysis to group the data and determine appropriate major characteristics to enhance the fuzzy clustering analysis method in terms of juvenile delinquency discriminant rules and prevention.
机译:近年来,数据挖掘技术在少年犯预防中的应用得到了不断的关注。机器学习的聚类算法可以根据分组目标将数据划分为许多群集,其主要目的是在数据中找到几个类似的群集。模糊聚类分析方法具有优异的聚类分配率。本研究将模糊C-MEAL(FCM)分析应用于对数据进行分组,并确定在少年犯歧视规则和预防方面提高模糊聚类分析方法的适当主要特征。

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