Comparative Analysis of Various Data Mining Classification Algorithms Using R Software
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
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Abstract
Data mining is the process of sorting the collection of stored data to identify/discover the patterns using some technical analysis. Evaluate the probability of future events data mining has more algorithms to segment and predict the data sets. Classification is one of the important mechanisms functioning by data mining and main goal of classification is to predict the target data accurately. For prediction numerous classification techniques have currently working, in this paper few of them are classified and compared with different data sets. Classification algorithms such as BayesNet, DT, J48, Logistic, Naïve Bayes, NBT, PART, RBFN are implemented and compared using R software with different test data.
References
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Keywords
Data mining, R classification, Classification comparison, Prediction, Accuracy, Quality Metrics.
