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A Comparative Analysis of Classification Algorithms on Weather Dataset Using Data Mining Tool

机译:数据挖掘工具对天气数据集分类算法的比较分析

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Data mining has become one of the emerging fields in research because of its vast contents. Data mining is used for finding hidden patterns in the database or any other information repository. This information is necessary to generate knowledge from the patterns. The main task is to extract knowledge out of the information. In this paper we use a data mining technique called classification to determine the playing condition based on the current temperature values. Classification technique is a powerful way to classify the attributes of the dataset into different classes. In our approach we use classification algorithms like Decision Tree (J48), REP Tree and Random Tree. Then we compare the efficiencies of these classification algorithms. The tool we use for this approach is WEKA (Waikato Environment for Knowledge Analysis) a collection of open source machine learning algorithms.
机译:数据挖掘由于其内容广泛而已成为研究中的新兴领域之一。数据挖掘用于查找数据库或任何其他信息存储库中的隐藏模式。该信息对于从模式中产生知识是必需的。主要任务是从信息中提取知识。在本文中,我们使用一种称为分类的数据挖掘技术,根据当前温度值确定比赛条件。分类技术是将数据集的属性分类为不同类别的有效方法。在我们的方法中,我们使用分类算法,例如决策树(J48),REP树和随机树。然后,我们比较了这些分类算法的效率。我们用于此方法的工具是WEKA(Waikato知识分析环境),它是开源机器学习算法的集合。

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