Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms
| Autor: | Aung Nway Oo |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
ComputingMethodologies_PATTERNRECOGNITION
C4.5 algorithm business.industry Process (engineering) Computer science Principal component analysis Knowledge engineering Decision tree Information technology Data mining (DM) Classification Decision Tree (DT) Principal component analysis (PCA) business Algorithm Random forest |
| Zdroj: | I. J. of Advances in Scientific Research and Engineering-IJASRE (ISSN: 2454-8006); Vol. 4 No. 6` (2018): Volume 4 Issue 6 June (2018); 122-126 |
| ISSN: | 2454-8006 |
| DOI: | 10.31695/ijasre.2018.32767 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|