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Item
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Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms
http://hdl.handle.net/20.500.12678/0000006174
http://hdl.handle.net/20.500.12678/000000617496bb7d09-6614-4e88-b037-b97a3a18a507
f9758039-9650-44fc-81ce-14e55f6622da
Name / File | License | Actions |
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Publication type | ||||||
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Journal article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms | |||||
Language | en | |||||
Publication date | 2018-06-01 | |||||
Authors | ||||||
Aung Nway Oo | ||||||
Description | ||||||
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. | ||||||
Keywords | ||||||
Data mining (DM), Classification, Decision Tree (DT), Principal component analysis (PCA) | ||||||
Identifier | 10.31695/IJASRE.2018.32767 | |||||
Journal articles | ||||||
6 | ||||||
International Journal of Advances in Scientific Research and Engineering (IJASRE) | ||||||
122-126 | ||||||
4 |