Analysis of Classification Algorithm in Data Mining
R. Aruna devi, K. Nirmala · International Journal of Data Mining Techniques and Applications · 2014
Data Mining is the extraction of hidden predictive information from large database. Classification is the process of finding a model that describes and distinguishes data classes or concept. This paper performs the study of prediction of class label using C4.5 and Naïve Bayesian algorithm.C4.5 generates classifiers expressed as decision trees from a fixed set of examples. The resulting tree is used to classify future samples.The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. C4.5 uses information gain to help it decide which attribute goes into a decision node. A Naïve Bayesian classifier is a simple probabilistic classifier based on applying Baye’s theorem with strong (naive) independence assumptions. Naive Bayesian classifier assumes that the effect of an attribute value on a given class is independent of the values of the other attribute. This assumption is called class conditional independence. The results indicate that Predicting of class label using Naïve Bayesian classifier is very effective and simple compared to C4.5 classifier.