Research Paper | Computer Science & Engineering | India | Volume 6 Issue 6, June 2017
Accuracy Improvement of C4.5 using K means Clustering
Driyani Rajeshinigo, J. Patricia Annie Jebamalar
Data mining is used to extract useful information from the vast amount of data. Classification is one of the techniques of data mining which will be used to predict the target attribute accurately from the knowledge it gained from the training data. There are various classifiers which among decision tree is very simple and most effective method. Accuracy of a classifier is how well it predicts the target attribute of test data correctly from the knowledge gained from the training set. In this paper, classification accuracy of C4.5 is improved with K means Clustering and adding the tested data dynamically to the training set. This improved C4.5 predicts the target variable with higher accuracy level with the help of K means clustering which is used to discretize the continuous data.
Keywords: Decision tree, C45, K-Means, Continuous data
Edition: Volume 6 Issue 6, June 2017
Pages: 2755 - 2758
How to Cite this Article?
Driyani Rajeshinigo, J. Patricia Annie Jebamalar, "Accuracy Improvement of C4.5 using K means Clustering", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20174834, Volume 6 Issue 6, June 2017, 2755 - 2758
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