Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms

Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms
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基于决策树算法的主成分分析（PCA）比较研究

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发表时间：
2018
期刊：
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通讯作者：
Aung Nway Oo
Aung Nway Oo
中科院分区：
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文献类型：
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作者：
Aung Nway Oo

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数据挖掘（DM）可以被看作是信息技术自然发展的结果。数据挖掘方法在计算机科学和知识工程中的作用非常重要。许多数据挖掘方法用于分类。分类是寻找描述和区分数据类或概念的模型的过程。决策树（DT）方法在分类问题中是最有用的。分析了基于主成分分析的决策树算法J48、分类回归树和随机森林的效率。
Data mining (DM) can be viewed as a result of the natural evolution of information technology. The role of data mining approach is very important in computer science and knowledge engineering. A number of data mining approaches are used for classification. Classification is the process of finding a model that describes and distinguishes data classes or concepts. The decision tree (DT) approach is most useful in the classification problem. The research work analyses the efficiency of the Principal Component Analysis (PCA) based decision tree algorithms, namely J48, Classification and Regression Tree (CART) and Random Forest.