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  1. University of Information Technology
  2. Faculty of Computer Science

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

http://hdl.handle.net/20.500.12678/0000006174
http://hdl.handle.net/20.500.12678/0000006174
96bb7d09-6614-4e88-b037-b97a3a18a507
f9758039-9650-44fc-81ce-14e55f6622da
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Comparative Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms.pdf (398 Kb)
Publication type
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
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