Comparison of Different Classification Techniques Using Different Datasets

In this paper different classification techniques of Data Mining are compared using diverse datasets from University of California, Irvine(UCI). Accuracy and time required for execution by each technique is observed. The Data Mining refers to extracting or mining knowledge from huge volume of data. Classification is an important data mining technique with broad applications. It classifies data of various kinds. Classification is used in every field of our life. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. This work has been carried out to make a performance evaluation of J48, MultilayerPerceptron, NaiveBayesUpdatable, and BayesNet classification algorithm. Naive Bayes algorithm is based on probability and j48 algorithm is based on decision tree. The paper sets out to make comparative evaluation of classifiers J48, MultilayerPerceptron, NaiveBayesUpdatable, and BayesNet in the context of Labour, Soyabean and Weather datasets. The experiments are carried out using weka 3.6 of Waikato University. The results in the paper demonstrate that the efficiency of j48 and Naive bayes is good.